Applied Science, Faculty of
Mechanical Engineering, Department of
DSpace
UBCV
Jin, Fan
Master of Applied Science - MASc
University of British Columbia
Edge trimming is a necessary machining operation to achieve the required dimension and surface quality of carbon fiber reinforced polymer (CFRP) components. Rapid tool wear occurs during the edge trimming process owing to the high strength and abrasive nature of the carbon fibers, leading to potential surface damage. To satisfy the production efficiency and part quality requirements, prediction and monitoring of tool wear progression are essential in edge trimming of CFRP. This thesis presents the tool wear prediction and monitoring in edge trimming of unidirectional (UD) and multidirectional (MD) CFRPs using the artificial neural network (ANN) and random forest (RF) algorithms. The objective is to achieve the tool wear prediction and monitoring under various process conditions with limited training data. For edge trimming of UD CFRP, the feature parameters of the machine learning models consist of the instantaneous process parameters, such as the instantaneous radial force, fiber cutting angle, and uncut chip thickness. As a result, the training data obtained at one radial and axial depth of cut are applicable for tool wear prediction at other radial cutting depths. The experimental data shows a high correlation between the instantaneous radial force and the tool flank wear length. The proposed method achieves the tool wear length prediction within 10% error under various edge trimming conditions. The experimental data from UD CFRP is used for tool wear monitoring in edge trimming of MD CFRP. The tool wear length ratios among the tool edge portions corresponding to different UD layers of the MD CFRP are experimentally identified. The overall radial force is predicted by individual force components from each UD layer, which are influenced by the cutting speed, feed rate, tool helix angle, and wear length distribution along the tool edge. The results show that the proposed model predicts the radial force in edge trimming of MD CFRP with different layer-up sequences within 20% error including the tool wear effect. The proposed method is able to perform tool wear monitoring in edge trimming of MD CFRP based on the force prediction with the predefined maximum tool wear length.
Tool Wear Prediction and Monitoring in Edge Trimming of Carbon Fiber Reinforced Polymer Using Machine Learning Methods by Fan Jin B.Eng., Tianjin University, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2021 © Fan Jin, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Tool Wear Prediction and Monitoring in Edge Trimming of Carbon Fiber Reinforced Polymer Using Machine Learning Methods submitted by Fan Jin in partial fulfillment of the requirements for the degree of Master of Applied Science in Mechanical Engineering Examining Committee: Dr. Xiaoliang Jin, Assistant Professor, Department of Mechanical Engineering, UBC Supervisor Dr. Yusuf Altintas, Professor, Department of Mechanical Engineering, UBC Supervisory Committee Member Dr. Jasmin Jelovica, Assistant Professor, Department of Civil Engineering, UBC Supervisory Committee Member iii Abstract Edge trimming is a necessary machining operation to achieve the required dimension and surface quality of carbon fiber reinforced polymer (CFRP) components. Rapid tool wear occurs during the edge trimming process owing to the high strength and abrasive nature of the carbon fibers, leading to potential surface damage. To satisfy the production efficiency and part quality requirements, prediction and monitoring of tool wear progression are essential in edge trimming of CFRP. This thesis presents the tool wear prediction and monitoring in edge trimming of unidirectional (UD) and multidirectional (MD) CFRPs using the artificial neural network (ANN) and random forest (RF) algorithms. The objective is to achieve the tool wear prediction and monitoring under various process conditions with limited training data. For edge trimming of UD CFRP, the feature parameters of the machine learning models consist of the instantaneous process parameters, such as the instantaneous radial force, fiber cutting angle, and uncut chip thickness. As a result, the training data obtained at one radial and axial depth of cut are applicable for tool wear prediction at other radial cutting depths. The experimental data shows a high correlation between the instantaneous radial force and the tool flank wear length. The proposed method achieves the tool wear length prediction within 10% error under various edge trimming conditions. The experimental data from UD CFRP is used for tool wear monitoring in edge trimming of MD CFRP. The tool wear length ratios among the tool edge portions corresponding to different UD layers of the MD CFRP are experimentally identified. The overall radial force is predicted by individual force components from each UD layer, which are influenced by the cutting speed, feed rate, tool helix angle, and wear length distribution along the tool edge. The results show that the iv proposed model predicts the radial force in edge trimming of MD CFRP with different layer-up sequences within 20% error including the tool wear effect. The proposed method is able to perform tool wear monitoring in edge trimming of MD CFRP based on the force prediction with the predefined maximum tool wear length. v Lay Summary Tool wear prediction and monitoring in edge trimming of carbon fiber reinforced polymer (CFRP) components are essential because excessive tool wear can cause poor surface quality or part damage. For production lines involving edge trimming of CFRP in aerospace and automotive industries, cutting tools have to be changed frequently to avoid excessive tool wear, which significantly influences the production efficiency and cost. Conventional methods of tool wear monitoring can only be applied to limited process conditions. This thesis presents two tool wear monitoring methods using machine learning algorithms. The method is applicable for tool wear monitoring in edge trimming of CFRP components when the fibers are aligned in the same (UD CFRP) or different (MD CFRP) directions. Based on the experimental data of tool wear length and cutting force measurements, the machine learning algorithms are able to perform tool wear prediction for edge trimming at different radial depths of cut for UD CFRP, and different layer-up sequences for MD CFRP with limited training data. vi Preface I performed the work presented in Chapters 3 and 4. I proposed methods, conducted experiments, developed machine learning models, and analyzed experimental results. Dr. Xiaoliang Jin was the supervisor for this work. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ..................................................................................................................................x List of Figures ............................................................................................................................... xi List of Symbols ........................................................................................................................... xiv List of Abbreviations ................................................................................................................. xix Acknowledgements ......................................................................................................................xx Dedication ................................................................................................................................... xxi Chapter 1: Introduction ................................................................................................................1 Chapter 2: Literature Review .......................................................................................................6 2.1 Overview ......................................................................................................................... 6 2.2 Tool Wear in Composite Machining Process ................................................................. 6 2.3 Tool Wear Prediction for Milling Metal Alloys ............................................................. 7 2.4 Tool Wear Prediction in Machining Composite ............................................................. 8 2.5 Conclusions ................................................................................................................... 10 Chapter 3: Tool Wear Prediction for Edge Trimming of Unidirectional CFRP ...................12 3.1 Overview ....................................................................................................................... 12 3.2 Indicator for Tool Wear Prediction ............................................................................... 12 3.3 Experimental Methods .................................................................................................. 14 viii 3.3.1 Experimental Setup ................................................................................................... 14 3.3.2 Experimental Design ................................................................................................. 19 3.4 Machine Learning Algorithms for Tool Wear Prediction............................................. 23 3.4.1 Feature Extraction ..................................................................................................... 23 3.4.2 Random Forest .......................................................................................................... 24 3.4.3 Artificial Neural Network ......................................................................................... 27 3.5 Results and Discussion ................................................................................................. 30 3.5.1 Correlation between Instantaneous Radial Force and Tool Wear ............................ 30 3.5.2 Effect of Hyperparameters on the Prediction Performance ...................................... 31 3.5.3 Performance Evaluation of Predictive Models ......................................................... 35 3.6 Conclusions ................................................................................................................... 38 Chapter 4: Tool Wear Monitoring for Edge Trimming of Multidirectional CFRP ..............40 4.1 Overview ....................................................................................................................... 40 4.2 Machine Learning Algorithms for Force Prediction in Edge Trimming of MD CFRP 42 4.2.1 Tool Wear Characteristics in Edge Trimming of MD CFRP ................................... 42 4.2.2 Machine Learning Models for Force Prediction in Edge Trimming of MD CFRP .. 48 4.3 Experimental Comparison and Discussions.................................................................. 51 4.3.1 Process Conditions for Experimental Comparison ................................................... 51 4.3.2 Hyperparameter Tuning for the Machine Learning Models ..................................... 54 4.3.3 Force Prediction from the Maximum Tool Wear Length ......................................... 56 4.4 Conclusions ................................................................................................................... 59 Chapter 5: Conclusions and Future Research Directions ........................................................61 5.1 Conclusions ................................................................................................................... 61 ix 5.2 Future Research Directions ........................................................................................... 62 Bibliography .................................................................................................................................64 Appendix: Mechanics Model Transforming Force Prediction in Edge Trimming of UD CFRP to UD Layer Force in Edge Trimming of MD CFRP ....................................................71 x List of Tables Table 3.1 Cutting parameters in UD CFRP edge trimming experiments. .................................... 20 Table 3.2 L16 orthogonal array for UD CFRP edge trimming experiments. ............................... 21 Table 3.3 Features and label parameter in the dataset. ................................................................. 23 Table 3.4 Performance of tool wear predictive models developed based on machine learning algorithms. .................................................................................................................................... 37 Table 3.5 Cutting conditions and tool wear predictions with experimental comparison under different radial depths of cut. (VBpd: predicted tool flank wear length; VB����pd: average value of predicted tool flank wear length; VBexp : experimentally measured tool flank wear length) ...... 38 Table 4.1 Process parameters in validation dataset from UD layers in MD CFRP workpiece. ... 52 Table 4.2 Tool wear lengths for UD layers in MD CFRP calculated with the identified average tool wear ratio. .............................................................................................................................. 53 Table 4.3 Comparison of the predicted force from the ANN model and the experimental results........................................................................................................................................................ 59 Table A.1 Comparison between instantaneous radial force of the 8th layer and 1/15 of the total instantaneous radial force in edge trimming of UD CFRP. .......................................................... 74 xi List of Figures Figure 1.1 Example of UD CFRP applications in civil engineering [8]. ........................................ 2 Figure 1.2 Examples of MD CFRP applications in automotive industries [9]. .............................. 2 Figure 1.3 Examples of CFRP usage in aerospace engineering: (a) [10]; (b) [11]. ........................ 2 Figure 1.4 Example of CFRP edge trimming process [13]. ............................................................ 3 Figure 1.5 Flow chart of research content. ..................................................................................... 5 Figure 3.1 Cutting force generation in orthogonal cutting of UD CFRP composites................... 13 Figure 3.2 Experimental setup of CFRP edge trimming. ............................................................. 15 Figure 3.3 An example of tool flank wear measurement for UD CFRP edge trimming. (Vc = 100 [m/min], fz= 0.025 [mm/tooth], θ = 15 [°]) .................................................................................. 15 Figure 3.4 Schematic view of edge trimming process using down-milling configuration. .......... 16 Figure 3.5 Flow chart of signal processing for the instantaneous radial force Fri at time instance ti . .................................................................................................................................................. 18 Figure 3.6 Instantaneous radial force with immersion angle at the middle location along the CFRP thickness ............................................................................................................................. 19 Figure 3.7 Instantaneous fiber cutting angle at different tool immersion angles with 45° fiber orientation. .................................................................................................................................... 20 Figure 3.8 Architecture of RF-based predictive model for tool wear. .......................................... 26 Figure 3.9 Example of a regression tree structure in RF-based model. ........................................ 27 Figure 3.10 Architecture of ANN-based predictive model for tool wear ..................................... 28 Figure 3.11 Tool wear and instantaneous radial force vs cutting length under different cutting conditions and fiber cutting angle. (a) Vc = 100 [m/min], fz= 0.05 [mm/tooth], β= 70 [°]; (b) Vc = xii 150 [m/min], fz= 0.1 [mm/tooth], β= 90 [°]; (c) Vc = 200 [m/min], fz= 0.075 [mm/tooth], β= 170 [°]; (d) Vc = 250 [m/min], fz= 0.025 [mm/tooth], β= 135 [°]....................................................... 31 Figure 3.12 MAE% of tool wear prediction with “feature bagging” size in RF model. ............... 34 Figure 3.13 MAE% of tool wear prediction with regression tree number in RF model. .............. 34 Figure 3.14 MAE% of tool wear prediction with neuron number of hidden layer in ANN model........................................................................................................................................................ 35 Figure 3.15 Comparison of predicted and measured tool wear lengths using RF model. ............ 36 Figure 3.16 Comparison of predicted and measured tool wear lengths using ANN model. ........ 36 Figure 4.1 Schematic view of a quasi-isotropic MD CFRP plate. ................................................ 40 Figure 4.2 Workflow of tool wear monitoring in edge trimming of CFRP based on force prediction ...................................................................................................................................... 42 Figure 4.3 Schematic of quasi-isotropic CFRP workpiece in edge trimming process. ................ 43 Figure 4.4 An example of tool flank wear along the cutting edge in edge trimming of MD CFRP. (Vc = 100 [m/min]; fz= 0.05 [mm/tooth]; No.: UD layer number; θlayer : fiber orientation of UD layer) ............................................................................................................................................. 44 Figure 4.5 Schematic of the relationship between tool flank wear length and tool geometry in edge trimming of a UD layer of CFRP. ........................................................................................ 45 Figure 4.6 Measurement results of tool wear length in edge trimming of MD CFRP: (a) Vc = 100[m/min], fz= 0.05[mm/tooth]; (b) Vc = 200[m/min], fz= 0.025[mm/tooth] ............................ 47 Figure 4.7 Ratios between tool wear lengths of UD layers and the corresponding maximum tool wear length: (a) Vc = 100[m/min], fz= 0.05[mm/tooth]; (b) Vc = 200[m/min], fz= 0.025[mm/tooth] ........................................................................................................................... 48 Figure 4.8 Architecture of RF-based predictive model for instantaneous radial force ................. 50 xiii Figure 4.9 Architecture of ANN-based predictive model for instantaneous radial force ............. 51 Figure 4.10 MAE% of force prediction with “feature bagging” size in RF model. ...................... 55 Figure 4.11 MAE% of force prediction with regression tree number in RF model. ..................... 55 Figure 4.12 MAE% of force prediction with neuron number in hidden layer of ANN model. .... 56 Figure 4.13 Measured and predicted instantaneous radial force under different conditions. (a) Vc = 100 [m/min], fz= 0.05 [mm/tooth], ϕ= 155[°]; (b) Vc = 100 [m/min], fz= 0.05 [mm/tooth], ϕ= 160[°]; (c) Vc = 100 [m/min], fz= 0.05 [mm/tooth], ϕ= 165[°]; (d) Vc = 200 [m/min], fz= 0.025 [mm/tooth], ϕ= 155[°]; (e) Vc = 200 [m/min], fz= 0.025 [mm/tooth], ϕ= 160[°]; (f) Vc = 200 [m/min], fz= 0.025 [mm/tooth], ϕ= 165[°]. .................................................................................. 58 xiv List of Symbols 𝛽𝛽 Fiber cutting angle 𝛾𝛾 Helix angle of cutting tool 𝛿𝛿𝑗𝑗, 𝛿𝛿𝑘𝑘 Errors in the hidden layer and the output layer 𝜁𝜁𝑗𝑗 , 𝜁𝜁𝑘𝑘 Bias value of the hidden layer and the output layer 𝜂𝜂 Learning rate of ANN algorithm 𝜃𝜃 Fiber orientation 𝜃𝜃𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 Fiber orientation for UD layer in MD CFRP 𝜎𝜎 Normal stress in the contact area between tool edge and UD CFRP 𝜙𝜙 Immersion angle 𝜙𝜙𝑖𝑖 Instantaneous immersion angle corresponding to 𝑡𝑡𝑖𝑖 𝜙𝜙𝑠𝑠, 𝜙𝜙𝑙𝑙 Start and exit immersion angle 𝜓𝜓 Lag angle caused by helix angle 𝜓𝜓𝑚𝑚 Angle between the elemental force in each segment and the force of the middle segment 𝜓𝜓𝑙𝑙 Angle between the radial force in each layer and the force of the middle layer 𝜔𝜔 Angular velocity of the cutting tool 𝑎𝑎𝑙𝑙 Radial depth of cut 𝑎𝑎𝑝𝑝 Axial depth of cut ∆𝑎𝑎 Thickness of each segment along the thickness of CFRP plate xv 𝑏𝑏 Split feature for best split finding procedure at internal nodes 𝑐𝑐1, 𝑐𝑐2 Mean value of 𝑦𝑦𝑏𝑏 in 𝑅𝑅1 and 𝑅𝑅2 𝑓𝑓𝑧𝑧 Feed rate ℎ Uncut chip thickness 𝑖𝑖 Input layer of ANN algorithm 𝑗𝑗 Hidden layer of ANN algorithm 𝑘𝑘 Output layer of ANN algorithm 𝑚𝑚 Number of nodes in the input layer 𝑛𝑛 Number of nodes in the hidden layer 𝑞𝑞1, 𝑞𝑞2, 𝑞𝑞3, 𝑞𝑞4, 𝑞𝑞5, 𝑞𝑞6 Terms presenting 𝐴𝐴1, 𝐵𝐵1, 𝐶𝐶1, 𝐴𝐴2, 𝐵𝐵2, 𝐶𝐶2 for least squares fitting 𝑠𝑠 Split point for best split finding procedure at internal nodes 𝑡𝑡 Rotating time of the cutting tool 𝑡𝑡𝑖𝑖 Time instance 𝑡𝑡𝑠𝑠, 𝑡𝑡𝑙𝑙 Rotating time of the cutting tool corresponding to 𝜙𝜙𝑠𝑠 and 𝜙𝜙𝑙𝑙 𝑢𝑢 Iteration number of ANN algorithm 𝑤𝑤𝑖𝑖𝑗𝑗 Weights between the input layer and the hidden layer 𝑤𝑤𝑗𝑗𝑘𝑘 Weights between the hidden layer and the output layer 𝑥𝑥 Feature vector of dataset 𝐷𝐷 𝑥𝑥𝑏𝑏, 𝑦𝑦𝑏𝑏 Feature vector and label in 𝑅𝑅1 and 𝑅𝑅2 𝑥𝑥𝑖𝑖 Feature vector in input layer of ANN algorithm 𝑥𝑥′ Feature vector of dataset 𝐷𝐷′ 𝑦𝑦 Label of dataset 𝐷𝐷 xvi 𝑦𝑦𝑗𝑗, 𝑦𝑦𝑘𝑘 Output of hidden layer and output layer of ANN algorithm 𝑦𝑦𝑙𝑙𝑒𝑒𝑝𝑝 Measured results from experiments 𝑦𝑦�𝑙𝑙𝑒𝑒𝑝𝑝 Mean value of measured results from experiments 𝑦𝑦𝑝𝑝𝑝𝑝 Results from predictive models 𝑦𝑦′ Label of dataset 𝐷𝐷′ 𝑧𝑧 Number of UD CFRP layer in MD CFRP 𝐴𝐴1, 𝐴𝐴2, 𝐵𝐵1, 𝐵𝐵2, 𝐶𝐶1, 𝐶𝐶2 Parameters in the sine forms of 𝐾𝐾𝑙𝑙𝑟𝑟 and 𝐾𝐾𝑙𝑙𝑙𝑙 𝐶𝐶𝑡𝑡 Tool geometry coefficient 𝐷𝐷 Dataset for tool wear predictive models for UD CFRP edge trimming process 𝐷𝐷𝐼𝐼 Subset generated by RF algorithm 𝐷𝐷′ Dataset for instantaneous radial force predictive models for multidirectional CFRP edge trimming process 𝐷𝐷𝑡𝑡 Diameter of cutting tool 𝐸𝐸 Young’s Modulus of UD CFRP 𝐹𝐹𝑓𝑓 Total feed force 𝐹𝐹𝑓𝑓1, 𝐹𝐹𝑓𝑓2 Feed force in chip formation region and pressing region 𝐹𝐹𝑚𝑚𝑙𝑙𝑙𝑙𝑚𝑚 Mean value of radial force 𝐹𝐹𝑚𝑚𝑙𝑙𝑝𝑝 Median value of radial force 𝐹𝐹𝑚𝑚𝑙𝑙𝑒𝑒 Maximum value of radial force 𝐹𝐹𝑠𝑠𝑡𝑡𝑝𝑝 Standard deviation of radial force 𝐹𝐹𝑙𝑙 Radial milling force xvii 𝐹𝐹𝑙𝑙𝑖𝑖 Instantaneous radial force corresponding to 𝑡𝑡𝑖𝑖 𝐹𝐹𝑙𝑙−𝑙𝑙𝑒𝑒𝑝𝑝 Instantaneous radial force from MD CFRP experimental results 𝐹𝐹𝑙𝑙−𝑅𝑅𝑅𝑅 Instantaneous radial force from the RF-based predictive model 𝐹𝐹𝑙𝑙−𝐴𝐴𝐴𝐴𝐴𝐴 Instantaneous radial force from the ANN-based predictive model 𝐹𝐹𝑙𝑙−𝑚𝑚𝑖𝑖𝑝𝑝 Radial force contributed by the middle layer in UD CFRP 𝐹𝐹𝑙𝑙−𝑝𝑝𝑝𝑝 Predicted radial force contributed by the UD layer in MD CFRP 𝐹𝐹𝑡𝑡 Tangential milling force 𝐹𝐹𝑡𝑡1, 𝐹𝐹𝑡𝑡2 Tangential force in chip formation region and pressing region 𝐹𝐹𝑋𝑋, 𝐹𝐹𝑌𝑌 Milling forces in feed and normal direction ∆𝐹𝐹𝑙𝑙 Radial force contributed by each segment along the thickness of CFRP plate 𝐻𝐻 Number of segments along the thickness of CFRP plate 𝐾𝐾𝑤𝑤 Tool wear coefficient 𝐾𝐾𝑙𝑙𝑟𝑟 Cutting force coefficient in radial direction 𝐾𝐾𝑙𝑙𝑙𝑙 Edge force coefficient in radial direction 𝑀𝑀 Size of “feature bagging” at internal nodes of the trees in RF algorithm 𝑀𝑀𝐴𝐴𝐸𝐸% Mean squared error in percentage 𝑁𝑁 Number of trees in RF algorithm Ne Size of data set for tool wear predictive models 𝑅𝑅1, 𝑅𝑅2 The pair of half-planes split by 𝑠𝑠 for values of the split feature 𝑅𝑅2 Coefficient of determination xviii 𝑇𝑇 Cutting period of the cutting tool 𝑉𝑉𝑟𝑟 Cutting speed 𝑉𝑉𝐵𝐵 Tool flank wear length 𝑉𝑉𝐵𝐵𝑙𝑙𝑒𝑒𝑝𝑝 Measured tool flank wear length 𝑉𝑉𝐵𝐵𝑝𝑝𝑝𝑝 Predicted tool flank wear length 𝑉𝑉𝐵𝐵����𝑝𝑝𝑝𝑝 Average value of predicted tool flank wear length 𝑉𝑉𝐵𝐵𝑚𝑚𝑙𝑙𝑒𝑒 Maximum tool flank wear length on the tool edge 𝑉𝑉𝐵𝐵𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 Tool flank wear length for UD layers in MD CFRP 𝑋𝑋, 𝑌𝑌 Feed and normal direction in CFRP edge trimming experiments xix List of Abbreviations AE Acoustic emission ANFIS Adaptive network-based fuzzy inference system ANN Artificial neural network BP Back-propagation CFRP Carbon fiber reinforced polymer FF Feed-forward FFT Fast Fourier transform GFRP Glass fiber reinforced polymer MD Multidirectional MRA Multiple regression analysis RF Random forest SVR Support vector regression UD Unidirectional xx Acknowledgements I owe particular thanks to my supervisor Dr. Xiaoliang Jin for his support and guidance throughout my thesis. His persistence and attitude towards research inspires me. It has been my honor and fortune to participate in his group in Advanced Manufacturing Processes Laboratory (AMP). I would also like to thank Dr. Yusuf Altintas for his support for this work of experimental equipment in Manufacturing Automation Laboratory (MAL). I acknowledge the Nature Science and Engineering Research Council (NSERC), Canada for funding this research work through Collaborative Research and Development grant. I offer my enduring gratitude to my friend Dr. Yan Bao for his valuable advice on technical details for this work. Our conversation encouraged me in the daily life during research and made me keep moving on the right track to carry out this work. Finally, special thanks are owed to my family, who have supported me throughout my years of education, both morally and financially. xxi Dedication To my grandmother 1 Chapter 1: Introduction Carbon fiber reinforced polymer (CFRP) composites have been increasingly used in various industrial applications, owing to their high specific strength, high stiffness, excellent corrosion resistance, and fatigue resistance [1-3]. The non-woven CFRP composites are divided into unidirectional (UD) and multidirectional (MD) categories based on the arrangement of the fiber orientations. In UD CFRP, the fibers are aligned along the same direction in the whole structure. UD CFRP is applied ideally for applications where the longitude strength is essential, such as the replacement for wood or metal restorations in bridges and buildings [4]. Figure 1.1 shows the UD CFRP straps used for strengthening the concrete slabs in a building structure. MD CFRP consists of multiple UD CFRP layers aligned at different fiber orientations to provide the required strengths in multiple directions. MD CFRP has been used in automotive industries, e.g., the ‘safety cell’ monocoque chassis assembly in racing cars [5]. Figure 1.2 shows the chassis of Lamborghini Aventador LP700-4 and the monocoque of McLaren P1 made of MD CFRP. Both UD and MD CFRP composites are of particular interest in aerospace structural components, owing to the lightweight requirement essential for improving fuel efficiency and reducing emissions [6]. The percentage of CFRP materials in the new generation of airplanes has significantly increased, reaching over 50% of the whole weight in some airplane models [7]. Figure 1.3 shows the usage information of CFRP components in Airbus 380 and Boeing 787 Dreamliner. 2 Figure 1.1 Example of UD CFRP applications in civil engineering [8]. Figure 1.2 Examples of MD CFRP applications in automotive industries [9]. Figure 1.3 Examples of CFRP usage in aerospace engineering: (a) [10]; (b) [11]. 3 Although different manufacturing techniques are used to produce near-net-shape CFRP components, post-machining operations such as edge trimming and milling are necessary to meet the dimensions and surface quality requirements [12]. Edge trimming is performed using side milling operation at a small radial depth of cut. An example of the edge trimming operation is shown in Figure 1.4. Figure 1.4 Example of CFRP edge trimming process [13]. Due to the high strength and abrasive nature of the carbon fibers, rapid tool wear occurs in the edge trimming process. This not only leads to poor surface finish or part damage, but also causes frequent tool change, which affects the production efficiency and costs [14-16]. In the machining process, cutting tools are often replaced in advance of the preset wear length thresholds in order to avoid the risk of part damage. However, this does not ensure an optimal cutting tool replacement and efficient machining operation. Therefore, it is necessary to develop tool wear prediction and monitoring techniques using the in-process sensor signals to enhance production efficiency with proper tool replacement time. 4 This thesis proposes the tool wear prediction and monitoring methods for edge trimming of UD and MD CFRP materials based on artificial neural network (ANN) and random forest (RF) algorithms. Instantaneous process parameters such as fiber cutting angle, uncut chip thickness, and cutting speed at various tool immersion angles are included in the feature parameters in data training. The fiber cutting angle is defined by the intersection angle between the direction of the cutting tool velocity and the fiber orientation [17, 18]. The instantaneous radial force is obtained from the measured cutting forces in the fixed machine coordinates and is used as the indicator of tool flank wear length. Therefore, the limited training data obtained from edge trimming experiments at one radial depth of cut are applicable for tool wear predictions at other cutting depths. The tool wear monitoring for edge trimming of MD CFRP is developed based on the experimental data of UD CFRP edge trimming. The tool wear length ratios among the tool edge portions corresponding to different UD layers of the MD CFRP are experimentally identified. The instantaneous radial force of each UD layer in the MD CFRP is obtained from the predefined tool wear length using the machine learning algorithm. The total cutting force is obtained in edge trimming of MD CFRP considering the layer-up sequence of MD CFRP, the cutting tool's helix angle, and the tool wear distribution along the edge. Therefore, the force prediction corresponding to the predefined maximum tool wear length can be used for tool life monitoring in edge trimming of MD CFRP by monitoring the cutting forces. The flow chart of the research in this thesis is illustrated in Figure 1.5. 5 Figure 1.5 Flow chart of research content. The remaining parts of this thesis are organized as follows. The literature review is presented in Chapter 2, including the existing research results for tool wear mechanism in machining CFRP, tool wear prediction methods based on data-driven methods in milling metal and CFRP. Chapter 3 presents the tool wear prediction in edge trimming of UD CFRP using the instantaneous radial force based on the machine learning method. Chapter 4 presents the force prediction in edge trimming of MD CFRP based on the predefined tool wear length. Chapter 5 concludes the results of this research and proposes possible future research directions. 6 Chapter 2: Literature Review 2.1 Overview Due to the inhomogeneous and heterogeneous properties of CFRP material, it is challenging to develop an accurate mechanics model for machining CFRP with the tool wear effect. Direct visual measurement of tool wear length requires frequent stops of the machine for the cutting tool inspection. Therefore, it has a negative effect on the cycle time and process efficiency in the production. Nevertheless, the indirect measurement using machine-integrated sensors provides real-time data such as cutting forces, vibrations, and acoustic emission signals, and the experimental data can be used to train machine learning models to determine the tool wear length under various machining conditions. In this chapter, previous research on tool wear mechanism in the CFRP machining process is introduced in section 2.1. Since edge trimming is in the form of side milling, section 2.2 discusses the tool wear prediction methods in milling of metal alloys, which is an important reference for edge trimming of composites. Section 2.3 presents the literature results of tool wear prediction and monitoring in the machining of CFRP materials. Section 2.4 summarizes certain limitations of the existing methods and presents the motivation of the study in this thesis. 2.2 Tool Wear in Composite Machining Process Machining of CFRP involves both tool edge chipping and abrasive tool wear caused by the high strength of the carbon fibers [19, 20]. Hanasaki et al. [21] explained that during the machining of CFRP, the carbon fibers break and slide on the cutting tool’s surface, causing abrasive wear on the rake and flank faces of the cutting tool. Khan and Kumar [22] conducted machining of glass 7 fiber reinforced polymer (GFRP) and found that the glass fibers scrape the tool edge and remove the tool materials at the flank face, causing abrasive wear on the cutting tool. Iliescu et al. [23] stated that the tool life criterion in drilling of CFRP could be represented by the ribbed bands formed on the clearance face of the drill. Liu et al. [24] found that the abrasive wear on the cutting tool is caused by the scratching of carbon fibers in the polymer matrix during CFRP drilling. Khairussaleh et al. [25, 26] performed milling experiments on CFRP, and observed that the cutting tool experience abrasive wear caused by the powder-type chips and carbon fibers. The flank wear length is used as the indicator to define the tool life because it directly influences the part geometry and the machined surface quality. Faraz et al. [27], Swan et al. [28], and Raj et al. [29] used tool edge rounding approximation for characterizing the change of tool edge geometry with flank wear in machining CFRP. The tool edge geometry with flank wear is approximated by a circular arc, and the equivalent tool edge radius increases with the tool wear progression in the machining process. The cutting conditions such as the cutting speed, feed rate, and fiber orientation significantly influence the tool wear progression in machining CFRP. Ozkan et al. [30] found that cutting speed and feed rate are the dominant factors for tool wear rate in milling CFRP, where the higher cutting speed and feed rate cause lower tool wear length due to the reduced contact time. Nguyen et al. [31] quantitatively determined the effect of fiber orientation on the tool wear length in milling CFRP, which shows 45° fiber orientation leads to the highest tool flank wear rate, while 0° fiber orientation results in the least tool wear rate. 2.3 Tool Wear Prediction for Milling Metal Alloys The edge trimming operation is in the form of side milling, so some related work on the tool wear prediction in milling of metal alloys are discussed. Cutting force signal has been widely used 8 as the main indicator of tool wear length due to its high reliability. Choudhury and Rath [32] determined the quantitative relationship between the average tangential force coefficient and tool flank wear in milling C45 steel. A 3-factorial design of experiment (DOE) was used to obtain the cutting force data at various cutting speeds, depths of cut, and feed rates. The results show a maximum error of about 8% in predicting the tool wear length. Chen and Chen [33] used ANN to develop an in-process tool wear prediction system for milling 1018 steel. The input variables considered in the model are feed rate, depth of cut, and average peak cutting force, and the ANN-based model was trained using 100 experimental data with a back-propagation algorithm. The results show that the model is able to predict the tool wear length with an average error of ±0.037 mm compared with the actual tool wear values. Palanisamy et al. [34] developed a regression model and a feed-forward back-propagation (FFBP) ANN model for predicting the tool flank wear in end milling of AISI 1020 steel. A 3-factorial DOE was used in the milling experiments at various feed rates, cutting speeds, and depths of cut. The results show that the ANN model achieves higher predictive accuracy than the regression model. Wu et al. [35] compared different machine learning algorithms such as ANN, support vector regression (SVR), and RF for the tool wear prediction in milling operations. The input variables include cutting force, vibration, and acoustic emission data. The experimental results show that RF can generate more accurate predictions than the other two machine learning algorithms. 2.4 Tool Wear Prediction in Machining Composite For the tool wear prediction in machining fiber-based composite materials, Azmi et al. [36] performed tool wear prediction in end milling of unidirectional GFRP composites. The mean values of cutting forces in feed and normal directions are measured and fed into multiple regression 9 analysis (MRA) and adaptive network-based fuzzy inference system (ANFIS), with cutting speed, feed rate, and fiber orientation used as the feature parameters. The results show that cutting forces in both feed and normal directions are able to predict tool wear with acceptable accuracy, and ANFIS results in more accurate predictions compared to MRA. Azmi [37] further improved the accuracy of ANFIS-based model by applying two data partitioning techniques, including grid partitioning and subtractive clustering. It is concluded that the ANFIS model is able to capture the nonlinear relationship between the tool wear length and the cutting force, with the prediction error smaller than 15%. Slamani et al. [38] measured cutting forces in feed, normal, and axial directions in edge trimming of quasi-isotropic CFRP plates under different cutting speeds and feed rates. The cutting condition including cutting speed, feed rate, and cutting length, the measurements of maximum flank wear and average resultant force are used to develop a multiplicative model and an exponential model for tool wear prediction, respectively. The results show that the multiplicative model performs more accurate predictions compared to the exponential model, with the coefficient of determination larger than 0.9. Rimpault et al. [39] applied adaptive fractal analyses on cutting force signals to determine the tool wear in edge trimming of quasi-isotropic CFRP. The experiment was conducted under three cutting conditions with different feed rates and cutting speeds, and the statistical features of the resultant forces were obtained. The tool wear length was represented by the average value of the maximum tool wear lengths on six flutes of the cutting tool. The results show that the fractal analysis is more effective than the statistic features of the cutting forces in tool wear prediction. Prakash et al. [40] monitored the wear of router tools in edge trimming of quasi-isotropic CFRP plates using acoustic emission (AE) sensor. The average values of the root mean square of AE signals (𝐴𝐴𝐸𝐸rms) were considered as an indirect measurement of the tool wear. The experimental results show the increase of 𝐴𝐴𝐸𝐸rms amplitude with the increase 10 of tool wear length. Prakash et al. [41] further performed linear regression analysis on the tool wear length as a function of spindle speed, feed rate, and tool hardness. Genetic algorithm and differential evolution algorithm were applied to determine the optimal cutting conditions with minimum tool wear rate. Caggiano et al. [42] applied sensor fusion technology for tool wear prediction in drilling of quasi-isotropic CFRP. The sensor signals collected in the drilling process include the thrust force and the torque. The time-domain signals were converted into the frequency domain, and sensor fusion feature pattern vectors were developed to predict the tool wear length. Furthermore, the relationship between the exit delamination factor and the tool wear progression was developed, which provides in-process guidance to support the decision of tool change execution. In another work, Caggiano et al. [43] collected thrust force, torque, and acoustic emission signals in drilling of CFRP of stacks, and constructed sensor fusion pattern vectors in order to predict tool wear using ANN-based pattern recognition paradigms. The results are used to monitor the tool wear progression and provide decisions of tool change execution. 2.5 Conclusions Data-driven approaches have been applied for tool wear prediction in edge trimming of the fiber-based composites. Cutting forces are widely used as the tool wear length indicator owing to their high reliability and consistency. In the literature, statistical features such as the mean or maximum values of the cutting forces in a number of tool rotating periods are recorded, with the data training experiments performed at different feed rates, cutting speeds, fiber orientations, and cutting depths. As a result, the machine learning methods provide a quantitative relationship between the cutting forces and the tool wear length for tool wear prediction. However, this method requires extensive experiments for data collection under various cutting conditions. This is 11 especially challenging for edge trimming of CFRP at different radial depths of cut, fiber orientations, and different layer-up sequences for MD CFRP. In order to reduce the material, machine, and labor costs associated with the experimental data training, this thesis proposes to use the instantaneous process parameters in the machine learning model, including the instantaneous uncut chip thickness, fiber cutting angle, and instantaneous radial force in the feature parameters and label, rather than the feed rate, fiber orientation, and mean cutting force. This enables the tool wear length prediction in edge trimming at different radial depths of cut without further data training. In addition, the experimental data from edge trimming of UD CFRP is used to predict the overall radial forces in edge trimming of MD CFRP, considering the tool wear length distribution along the tool edge caused by the UD layers with different fiber orientations. The quantitative relationship developed between the maximum tool wear length along the tool edge and the radial force can be used for tool wear monitoring from the force measurement. This method does not require additional data training when the layer-up sequence in the MD CFRP changes. As a result, the study in this thesis is expected to achieve the tool wear prediction and monitoring in edge trimming of CFRP at different radial depths of cut and different layer-up sequences with limited data training, therefore reducing the associated experiment costs. 12 Chapter 3: Tool Wear Prediction for Edge Trimming of Unidirectional CFRP 3.1 Overview The relationship between the tool wear length and the cutting parameters in edge trimming of CFRP is highly nonlinear. This chapter presents the tool wear prediction in edge trimming of UD CFRP using machine learning methods. Cutting force and tool wear length data are measured with respect to the cutting length under different cutting speeds, feed rates, and fiber orientations in the edge trimming experiments. Artificial neural network (ANN) and random forest (RF) are used for data training and tool wear length prediction. Different from the previous work in the literature, instantaneous forces are used as the tool wear indicator for tool wear prediction, and the feature parameters used in the machine learning algorithms are based on instantaneous parameters such as uncut chip thickness, fiber cutting angle, and cutting speed. The prediction performance of the proposed method is evaluated at other feature parameters and other radial depths of cut in edge trimming process. 3.2 Indicator for Tool Wear Prediction Figure 3.1 illustrates the schematic and force generation in orthogonal cutting of UD CFRP [44]. The cutting area is divided into two deformation regions: the chip formation region and the pressing region. The tangential force 𝐹𝐹𝑡𝑡1 and the feed force 𝐹𝐹𝑓𝑓1 in the chip formation region are dependent on the uncut chip thickness. On the other hand, the force components 𝐹𝐹𝑡𝑡2 and 𝐹𝐹𝑓𝑓2 in the pressing region increase with the tool wear length due to larger contact area between the workpiece material and the clearance face of the cutting tool. In addition, the anisotropy and inhomogeneity of CFRP result in different material removal mechanisms such as buckling, fiber bending, and 13 matrix shearing when the fiber orientation changes. Therefore, the cutting forces are dependent on the uncut chip thickness, cutting speed, fiber orientation, and tool wear length. Figure 3.1 Cutting force generation in orthogonal cutting of UD CFRP composites. As discussed in Chapter 2, most research in the literature used average cutting forces as an indicator for tool wear prediction. However, based on Karpat et al. [17] and Mullin et al. [18], the cutting force coefficient which is the ratio between the cutting force and the uncut chip area is a function of the fiber cutting angle. As a result, under the same cutting speed and feed per tooth, different tool wear lengths may result in the same average force at different ranges of fiber cutting angles. Therefore, in this thesis, the instantaneous force directly influenced by the fiber cutting angle is proposed as the indicator for tool wear prediction. The radial force, corresponding to the total feed force 𝐹𝐹𝑓𝑓 in orthogonal cutting, is chosen due to its higher sensitivity to the tool wear length [12]. The instantaneous radial force is independent of the radial depth of cut in the edge trimming process because it is determined by the uncut chip thickness and the fiber cutting angle. Therefore, the data collected in the experiment with a constant radial depth of cut can be used for 14 tool wear prediction at other radial depths. This reduces the size of data training for different radial depths of cut in the edge trimming process. 3.3 Experimental Methods 3.3.1 Experimental Setup Unidirectional CFRP composites with fiber model UTS-700 and epoxy resin NCT 304-1 were used as the workpiece specimen for the edge trimming experiments. The size of the CFRP workpiece is 50.8 mm × 152.4 mm × 4.8 mm. The fiber volume fraction is 55%, and the approximate density of the CFRP is 1.55 g/cm3. Indexable milling cutter (Sandvik Coromant: R390-025A25-11L) with 25 mm diameter and uncoated carbide inserts (R390-11 T3 04E-NL H13A) were used as the edge trimming tool. Only one insert was mounted on the cutter for wear investigation in order to reduce the tool condition variations with multiple inserts. A FADAL VMC-2216 3-axis CNC vertical machining center was used for the experiments, with the experimental setup shown in Figure 3.2. Continuous edge trimming was repeated along the feed direction with the same radial depth of cut. No coolant was used in the edge trimming experiments. Kistler 9258B dynamometer was used for force measurement in feed (X) and normal (Y) directions. National Instrument data acquisition card at a sampling rate of 51.2 kHz and CutPro software were used to collect the cutting force data. The edge profile of the cutting insert was examined using an optical microscope for tool wear measurement. Tool flank wear values at six different locations along the insert edge were measured, and the average value was determined to be the tool wear result. Figure 3.3 shows an example of the tool edge image with flank wear, and the measured tool wear results at different edge locations. 15 Figure 3.2 Experimental setup of CFRP edge trimming. Figure 3.3 An example of tool flank wear measurement for UD CFRP edge trimming. (𝑽𝑽𝒄𝒄 = 100 [m/min], 𝒇𝒇𝒛𝒛 = 0.025 [mm/tooth], 𝜽𝜽 = 15 [°]) 16 Figure 3.4 illustrates the schematic configuration of the edge trimming process within a single tool pass. Down-milling is chosen in the edge trimming experiments as it achieves better surface quality compared to up milling [45]. The radial depth of cut 𝑎𝑎𝑙𝑙 is 2 mm, and the axial depth of cut 𝑎𝑎𝑝𝑝 is equivalent to the thickness of the CFRP plate (4.8 mm). 𝐹𝐹𝑡𝑡 and 𝐹𝐹𝑙𝑙 are the tangential and the radial forces respectively, obtained from the measured forces in feed (𝑋𝑋) and normal (𝑌𝑌) directions, expressed as: 𝐹𝐹𝑡𝑡 = 𝐹𝐹𝑋𝑋 cos (𝜋𝜋 − 𝜙𝜙) + 𝐹𝐹𝑌𝑌 sin (𝜋𝜋 − 𝜙𝜙) (3.1) 𝐹𝐹𝑙𝑙 = −𝐹𝐹𝑋𝑋 sin(𝜋𝜋 − 𝜙𝜙) + 𝐹𝐹𝑌𝑌 cos(𝜋𝜋 − 𝜙𝜙) (3.2) where 𝜙𝜙 is defined as the immersion angle of the insert, expressed as 𝜙𝜙 = 𝜔𝜔𝑡𝑡, and 𝜔𝜔 [rad/s] is the angular velocity of the cutting tool. Figure 3.4 Schematic view of edge trimming process using down-milling configuration. 17 Figure 3.5 shows the flow chart of signal processing to obtain the instantaneous radial force. The time-domain cutting forces in a period of 5 seconds are recorded, as shown in Figure 3.5(b). The time period for the forces data collection is close to the end of the trimming path, in order to ensure that the forces reflect the tool wear status in the wear length measurement after each trimming path. A Butterworth low-pass filter with the cut-off frequency of 1000 Hz is applied to the time-domain signal to reduce the influence of the measurement or vibration noises, with the filtered data shown in Figure 3.5(c). In Figure 3.5(d), the force signal in non-cutting period is set to be zero to remove the direct current noise of the signal. Because of the helix angle of the insert, there is an entry process when the insert does not cut into the workpiece completely, which is also removed in order to calculate the radial force corresponding to the cutting geometry shown in Figure 3.4. Assume 𝜙𝜙𝑠𝑠 and 𝜙𝜙𝑙𝑙 are the entry and exit tool immersion angles, which are mapped to the time instances when the middle location of the insert cuts into (𝑡𝑡𝑠𝑠) and out (𝑡𝑡𝑙𝑙) of the workpiece. In Figure 3.5(e), the radial milling force is calculated based on Eq. (3.2), where the instantaneous immersion angle 𝜙𝜙𝑖𝑖 refers to the tool edge at the middle location along the thickness of the CFRP workpiece, as shown in Figure 3.6. The mapping between 𝜙𝜙𝑖𝑖 and the instantaneous radial force 𝐹𝐹𝑙𝑙𝑖𝑖 is performed from the corresponding time instance 𝑡𝑡𝑖𝑖, as shown in Eq. (3.3). 𝜙𝜙𝑖𝑖 − 𝜙𝜙𝑠𝑠𝜙𝜙𝑙𝑙 − 𝜙𝜙𝑖𝑖= 𝑡𝑡𝑖𝑖𝑇𝑇(3.3) where T is the cutting period calculated by 𝑇𝑇 = (𝜙𝜙𝑙𝑙 − 𝜙𝜙𝑠𝑠)/𝜔𝜔. 18 Figure 3.5 Flow chart of signal processing for the instantaneous radial force 𝑭𝑭𝒓𝒓𝒓𝒓 at time instance 𝒕𝒕𝒓𝒓. 19 Figure 3.6 Instantaneous radial force with immersion angle at the middle location along the CFRP thickness: (a) 3D view; (b) top view. 3.3.2 Experimental Design The instantaneous radial forces and the insert flank wear are dependent on the cutting speed, uncut chip thickness, and fiber cutting angle. In edge trimming of UD CFRP, the fiber orientation 𝜃𝜃 is defined as the intersection angle between the feed motion of the machine and the fiber orientation. As a result, the instantaneous fiber cutting angle 𝛽𝛽 is determined by the instantaneous tool immersion angle 𝜙𝜙 which changes continuously as the cutting tool rotates, and the fiber orientation 𝜃𝜃, expressed as Eq. (3.4) [17]. �𝛽𝛽 = 𝜃𝜃 + 𝜙𝜙 if 𝛽𝛽 < 180 𝛽𝛽 = 𝑚𝑚𝑚𝑚𝑚𝑚(𝛽𝛽, 180) if 𝛽𝛽 ≥ 180 (3.4) Figure 3.7 presents the instantaneous fiber cutting angle 𝛽𝛽 at different tool immersion angles 𝜙𝜙 for 45˚ fiber orientation 𝜃𝜃 as an example. 20 Figure 3.7 Instantaneous fiber cutting angle at different tool immersion angles with 45° fiber orientation. The edge trimming experiments were conducted under dry condition with different cutting speeds 𝑉𝑉𝑟𝑟, feed rates 𝑓𝑓𝑧𝑧, and fiber orientations 𝜃𝜃. Each process parameter includes four levels, with the corresponding values in each level used in the experiments shown in Table 3.1. Table 3.1 Cutting parameters in UD CFRP edge trimming experiments. Cutting Parameters Levels Cutting speed 𝑉𝑉𝑟𝑟 [m/min] 100, 150, 200, 250 Feed rate 𝑓𝑓𝑧𝑧 [mm/tooth] 0.025, 0.05, 0.075, 0.1 Fiber orientation 𝜃𝜃 [°] 15, 90, 105, 150 Based on the edge trimming parameters, the design of experiment method with an L16 orthogonal array is used to define each specific trimming experiment, with the process parameters shown in Table 3.2. With a radial depth of cut 𝑎𝑎𝑙𝑙 of 2mm, the immersion angle corresponding to the in-cutting period is in the range between 𝜙𝜙𝑠𝑠 = 147° and 𝜙𝜙𝑙𝑙 = 180°. The instantaneous radial 21 forces at six tool immersion angles of 155°, 157.5°, 160°, 162.5°, 165°, 167.5° were measured. The instantaneous uncut chip thickness is determined by ℎ = 𝑓𝑓𝑧𝑧 ∙ sin𝜙𝜙, and the fiber cutting angle 𝛽𝛽 is calculated from Eq. (3.4), with the values at every tool immersion angle listed in Table 3.2 for all the cutting conditions. After each trimming path of 152.4 mm (length of the CFRP plate in the feed direction), the flank wear length of the insert was measured, and a total of 10 trimming passes were conducted at each cutting condition. As a result, the total dataset includes Ne = 16 (number of cutting condition) × 6 (number of immersion angle) × 10 (number of tool wear measurement at each cutting condition) = 960 sets. Each set is composed of the cutting speed, uncut chip thickness, fiber cutting angle, instantaneous cutting force, and tool wear length. It should be noted that instead of feed rate 𝑓𝑓𝑧𝑧 and fiber orientation 𝜃𝜃, the instantaneous parameters including the uncut chip thickness ℎ and the fiber cutting angle 𝛽𝛽 are used in the dataset, and consist of the feature parameters for data training in the machine learning algorithms. Therefore, even the edge trimming experiments are conducted only at radial depth of cut 𝑎𝑎𝑙𝑙 of 2 mm, the results are applicable to predict the tool wear length at other radial depths of cut, as long as the instantaneous radial forces and the corresponding uncut chip thickness and fiber cutting angle are identified. Therefore, the size of the dataset can be reduced substantially, and the developed models are applicable for edge trimming conditions with other cutting depths, feed speeds, and fiber orientations. Table 3.2 L16 orthogonal array for UD CFRP edge trimming experiments. No. Process Parameters Instantaneous Parameters 𝑽𝑽𝒄𝒄 [m/min] 𝒇𝒇𝒛𝒛 [mm/tooth] 𝜽𝜽 [°] 𝝓𝝓 [°] 155 157.5 160 162.5 165 167.5 1 100 0.025 15 𝒉𝒉[μm] 10.57 9.58 8.55 7.52 6.47 5.41 𝜷𝜷[°] 170 172.5 175 177.5 0 2.5 22 2 100 0.05 90 𝒉𝒉[μm] 21.13 19.13 17.10 15.04 12.94 10.82 𝜷𝜷[°] 65 67.5 70 72.5 75 77.5 3 100 0.075 105 𝒉𝒉[μm] 31.69 28.70 25.65 22.55 19.41 16.23 𝜷𝜷[°] 80 82.5 85 87.5 90 92.5 4 100 0.1 150 𝒉𝒉[μm] 42.26 38.27 34.20 30.07 25.88 21.64 𝜷𝜷[°] 125 127.5 130 132.5 135 137.5 5 150 0.025 90 𝒉𝒉[μm] 10.57 9.58 8.55 7.52 6.47 5.41 𝜷𝜷[°] 65 67.5 70 72.5 75 77.5 6 150 0.05 15 𝒉𝒉[μm] 21.13 19.13 17.10 15.04 12.94 10.82 𝜷𝜷[°] 170 172.5 175 177.5 0 2.5 7 150 0.075 150 𝒉𝒉[μm] 31.69 28.70 25.65 22.55 19.41 16.23 𝜷𝜷[°] 125 127.5 130 132.5 135 137.5 8 150 0.1 105 𝒉𝒉[μm] 42.26 38.27 34.20 30.07 25.88 21.64 𝜷𝜷[°] 80 82.5 85 87.5 90 92.5 9 200 0.025 105 𝒉𝒉[μm] 10.57 9.58 8.55 7.52 6.47 5.41 𝜷𝜷[°] 80 82.5 85 87.5 90 92.5 10 200 0.05 150 𝒉𝒉[μm] 21.13 19.13 17.10 15.04 12.94 10.82 𝜷𝜷[°] 125 127.5 130 132.5 135 137.5 11 200 0.075 15 𝒉𝒉[μm] 31.69 28.70 25.65 22.55 19.41 16.23 𝜷𝜷[°] 170 172.5 175 177.5 0 2.5 12 200 0.1 90 𝒉𝒉[μm] 42.26 38.27 34.20 30.07 25.88 21.64 𝜷𝜷[°] 65 67.5 70 72.5 75 77.5 13 250 0.025 150 𝒉𝒉[μm] 10.57 9.58 8.55 7.52 6.47 5.41 𝜷𝜷[°] 125 127.5 130 132.5 135 137.5 14 250 0.05 105 𝒉𝒉[μm] 21.13 19.13 17.10 15.04 12.94 10.82 𝜷𝜷[°] 80 82.5 85 87.5 90 92.5 15 250 0.075 90 𝒉𝒉[μm] 31.69 28.70 25.65 22.55 19.41 16.23 𝜷𝜷[°] 65 67.5 70 72.5 75 77.5 16 250 0.1 15 𝒉𝒉[μm] 42.26 38.27 34.20 30.07 25.88 21.64 𝜷𝜷[°] 170 172.5 175 177.5 0 2.5 23 3.4 Machine Learning Algorithms for Tool Wear Prediction 3.4.1 Feature Extraction In this thesis, two machine learning algorithms including Random Forest (RF) and Artificial Neural Network (ANN) are used for data training and tool wear length prediction. In the machine learning algorithm, feature extraction is an essential preprocessing procedure, where the original experimental data is converted into the format supported by the machine learning methods. The input of the machine learning algorithms is expressed by the labeled dataset as: 𝐷𝐷 = {(𝑥𝑥, 𝑦𝑦)}, where 𝑥𝑥 is the vector of the feature parameters, including the cutting speed, uncut chip thickness, fiber cutting angle, and the statistic description of instantaneous radial force including the mean, median, maximum, and standard deviation, and 𝑦𝑦 is the label that represents the measured tool flank wear length at different cutting lengths corresponding to the radial forces. All the features and the label in the dataset and their respective units are listed in Table 3.3. Table 3.3 Features and label parameter in the dataset. Symbols Units Features Instantaneous Radial Force - Mean 𝐹𝐹𝑚𝑚𝑙𝑙𝑙𝑙𝑚𝑚 N Instantaneous Radial Force - Median 𝐹𝐹𝑚𝑚𝑙𝑙𝑝𝑝 N Instantaneous Radial Force - Maximum 𝐹𝐹𝑚𝑚𝑙𝑙𝑒𝑒 N Instantaneous Radial Force - Standard Deviation 𝐹𝐹𝑠𝑠𝑡𝑡𝑝𝑝 N Cutting Speed 𝑉𝑉𝑟𝑟 m/min Uncut Chip Thickness ℎ μm Fiber Cutting Angle 𝛽𝛽 ° Label Tool Wear Length 𝑉𝑉𝐵𝐵 μm 24 3.4.2 Random Forest Random forest is an ensemble learning method which builds a forest of decision trees by bootstrapping samples from a training dataset. In the developed random forest structure, each decision tree generates a responding result as its prediction value. The architecture of the RF-based predictive model used in this chapter is illustrated in Figure 3.8. Because tool wear represents the gradual failure of cutting tool edge, and the prediction results of tool wear length should change continuously with the cutting length, each individual tree in RF for tool wear length prediction is referred to as a regression tree. Each internal node of the regression tree stands for one variable in the features of the created dataset, and each branch connecting to the internal node stands for a choice of the test on the internal node, and the leaf nodes at the end of branches represent a regression response [46]. Bootstrap aggregating or bagging is the first step to build one regression tree. Through random sampling from the original training dataset 𝐷𝐷 = {(𝑥𝑥, 𝑦𝑦)} with replacement based on the feature and label parameters in Table 3.3, the bagging process creates 𝑁𝑁 new training subset 𝐷𝐷𝐼𝐼, which is defined as a bagging sample. Then there are 𝑁𝑁 regression trees in the random forest. The number of trees used in the RF algorithm can range from hundreds to thousands. A smaller number of the trees in RF can lead to a larger error between the actual tool wear length and the predictions, and overfitting cannot be avoided effectively. However, if the number of trees becomes too large, the error will not decrease effectively, while the computation efficiency reduces. In this study, the number of regression trees generated in the RF algorithm is selected from 500 to 4000 with an interval of 100 to determine the optimal tree number. For each bootstrap aggregating training subset 𝐷𝐷𝐼𝐼, an un-pruned regression tree grows by randomly selecting 𝑀𝑀 features at each internal node, and the best split is chosen among the 25 sampled features to reduce the correlation of the trees. This procedure is named as “feature bagging”. In this chapter, the feature size 𝑀𝑀 for “feature bagging” is selected in a range from 2 to 7 according to the feature number in Table 3.3. The best split is chosen at each internal node with the following splitting procedure [47]. Considering a split feature 𝑏𝑏 and split point 𝑠𝑠, the pair of half-planes for values of the split feature is defined as: 𝑅𝑅1(𝑏𝑏, 𝑠𝑠) = {𝑥𝑥|𝑥𝑥𝑏𝑏 ≤ 𝑠𝑠} and 𝑅𝑅2(𝑏𝑏, 𝑠𝑠) = {𝑥𝑥|𝑥𝑥𝑏𝑏 ≥ 𝑠𝑠} (3.5) The split feature 𝑗𝑗 and split point 𝑠𝑠 should meet the requirement that: min𝑏𝑏,𝑠𝑠 � � (𝑦𝑦𝑏𝑏 − 𝑐𝑐1)2𝑒𝑒𝑏𝑏∈𝑅𝑅1(𝑏𝑏,𝑠𝑠) + � (𝑦𝑦𝑏𝑏 − 𝑐𝑐2)2𝑒𝑒𝑏𝑏∈𝑅𝑅2(𝑏𝑏,𝑠𝑠) � (3.6) where 𝑐𝑐1 and 𝑐𝑐2 are achieved respectively by 𝑐𝑐1 = 𝑎𝑎𝑎𝑎𝑎𝑎�𝑦𝑦𝑏𝑏�𝑥𝑥𝑏𝑏 ∈ 𝑅𝑅1(𝑏𝑏, 𝑠𝑠)� and 𝑐𝑐2 = 𝑎𝑎𝑎𝑎𝑎𝑎�𝑦𝑦𝑏𝑏�𝑥𝑥𝑏𝑏 ∈ 𝑅𝑅2(𝑏𝑏, 𝑠𝑠)� (3.7) For a regression tree, the training subset 𝐷𝐷𝐼𝐼 is split into two child nodes at the root node, and the splitting feature and splitting point are solved based on Eqs. (3.6) and (3.7). This procedure repeats for the regression tree at each following internal node continuously, and stops until the size of the subset on the leaf node decreases below a threshold value. A larger threshold means the size of subset on the leaf nodes is larger, which results in a larger prediction error, while a too small threshold value limits the generalization of the model prediction for the process conditions out of the training dataset. In this research, the threshold is set as 1 due to the limited number of the training data. The sensitivity of the predictive performance of the RF model with respect to the tree number and the “feature bagging” size is presented in section 3.5.2. 26 A regression tree is constructed from the root node of the tree, then a series of tests with the feature values at each node are performed. The organization of tests in a regression tree structure is shown in Figure 3.9, which indicates the example of the “feature bagging” and nodes generation criteria. After all the regression trees are constructed, a predictive value at a new feature vector 𝑥𝑥 is obtained through averaging the prediction values for 𝑥𝑥 from each of the individual regression trees, expressed as: 𝑓𝑓(𝑥𝑥) = 1𝑁𝑁�𝑓𝑓𝐼𝐼(𝑥𝑥)𝐴𝐴𝐼𝐼=1(3.8) where 𝑓𝑓 represents the output of the tool wear length corresponding to the process parameters represented by the feature vector 𝑥𝑥. Figure 3.8 Architecture of RF-based predictive model for tool wear. 27 Figure 3.9 Example of a regression tree structure in RF-based model. 3.4.3 Artificial Neural Network Artificial neural network is an algorithm which uses a set of connected nodes called artificial neurons to construct a network. The output signal of an artificial neuron is calculated by nonlinear functions of the summation of its input signals. A number of neurons are integrated to form a layer, and multiple layers generate the output signals using different functions [48]. The tool wear prediction is essentially a nonlinear regression problem. Therefore, the feed-forward (FF) neural network with back-propagation (BP) training method is used in this thesis, with the architecture illustrated in Figure 3.10. There are three layers in the ANN model: the input layer with seven neurons including the feature vector elements (𝐹𝐹𝑚𝑚𝑙𝑙𝑙𝑙𝑚𝑚, 𝐹𝐹𝑚𝑚𝑙𝑙𝑝𝑝, 𝐹𝐹𝑚𝑚𝑙𝑙𝑒𝑒, 𝐹𝐹𝑠𝑠𝑡𝑡𝑝𝑝, 𝑉𝑉𝑟𝑟, ℎ, 𝜃𝜃) listed in Table 3.3, the hidden layer, and the output layer with one neuron represented by the tool flank wear length 𝑉𝑉𝐵𝐵. The signal flow in the model is represented by the linking arrows between the neurons. The number of neurons in the hidden layer is tuned by grid search method to achieve the best prediction performance. The effect of the neuron number in the hidden layer on the prediction performance is analyzed in section 3.5.2. 28 Figure 3.10 Architecture of ANN-based predictive model for tool wear The interconnection which links the neurons between two neighboring layers is defined as a weight 𝑤𝑤, with the initial value randomly selected in the range from 0 to 1. A neuron in the hidden layer sums up all the weighted input values after adding a bias value 𝜁𝜁 , generating a value considered as the input of activation function 𝑔𝑔(𝑥𝑥) to determine its output. Between the hidden and output layers, a similar algorithm is performed without the activation function. Due to the nonlinearity between the tool wear length and the process parameters, the tan-sigmoid function is selected as the activation function, expressed as: 𝑔𝑔(𝑥𝑥) = 21 + 𝑎𝑎−2𝑒𝑒 − 1 (3.9) The outputs 𝑦𝑦𝑗𝑗 and 𝑦𝑦𝑘𝑘 of the hidden layer 𝑗𝑗 and output layer 𝑘𝑘 are shown as Eqs. (3.10) and (3.11): 29 𝑦𝑦𝑗𝑗 = 𝑔𝑔 ��𝑤𝑤𝑖𝑖𝑗𝑗𝑥𝑥𝑖𝑖𝑚𝑚𝑖𝑖=1+ 𝜁𝜁𝑗𝑗� (3.10) 𝑦𝑦𝑘𝑘 = �𝑤𝑤𝑗𝑗𝑘𝑘𝑂𝑂𝑗𝑗 + 𝜁𝜁𝑘𝑘𝑚𝑚𝑗𝑗=1(3.11) where 𝑥𝑥𝑖𝑖 is the value of the input node, 𝑚𝑚 = 7 is the number of nodes in the input layer, 𝑛𝑛 is the number of nodes in the hidden layer. The BP training method adjusts the weights in the neurons to minimize the mean squared error between the predicted and the measured tool wear length in data training. When the error between the predicted and the measured output is larger than the predefined threshold value, the weights of the nodes are adjusted by: 𝑤𝑤𝑖𝑖𝑗𝑗(𝑢𝑢 + 1) = 𝑤𝑤𝑖𝑖𝑗𝑗(𝑢𝑢) + 𝜂𝜂𝛿𝛿𝑗𝑗𝑦𝑦𝑗𝑗 (3.12) 𝑤𝑤𝑗𝑗𝑘𝑘(𝑢𝑢 + 1) = 𝑤𝑤𝑗𝑗𝑘𝑘(𝑢𝑢) + 𝜂𝜂𝛿𝛿𝑘𝑘𝑦𝑦𝑘𝑘 (3.13) where 𝜂𝜂 is the learning rate, a step size at each iteration moves forward to minimize the error, and 𝑢𝑢 is the iteration number. The learning rate 𝜂𝜂 used in this research is 0.001. 𝛿𝛿𝑘𝑘 and 𝛿𝛿𝑗𝑗 are the errors in the output layer 𝑘𝑘 and the hidden layer 𝑗𝑗, solved by Eq. (3.14) and (3.15). 𝛿𝛿𝑘𝑘 = 𝑦𝑦(1 − 𝑦𝑦𝑘𝑘)(𝑦𝑦 − 𝑦𝑦𝑘𝑘) (3.14) 𝛿𝛿𝑗𝑗 = 𝑦𝑦𝑗𝑗�1 − 𝑦𝑦𝑗𝑗��𝛿𝛿𝑘𝑘𝑤𝑤𝑗𝑗𝑘𝑘𝑘𝑘=1(3.15) The updating process of the weights 𝑤𝑤 stops until the mean squared error between the desired output 𝑦𝑦 and the calculated output 𝑦𝑦𝑘𝑘 in the iterations cannot reach the preset limit 0.001, or the interaction number achieves the limit 5000. After the weights of the hidden layer 𝜔𝜔𝑖𝑖𝑗𝑗 and 𝜔𝜔𝑗𝑗𝑘𝑘 are updated, one back-propagation training cycle is complete. After a training cycle, a new training pattern is brought to the network to repeat the training cycle. When the updating process of weights 30 𝑤𝑤 stops, the data training of the network is complete, and the values of the training parameters are recorded. 3.5 Results and Discussion 3.5.1 Correlation between Instantaneous Radial Force and Tool Wear A total of 160 trimming experiments were conducted under 16 different cutting conditions. A dataset of size 960 was built, with each set including seven feature parameters and one label parameter. Figure 3.11 presents the experimental results of the tool wear length and the corresponding instantaneous radial force with respect to the cutting length under four edge trimming conditions. The figures show similarities in the growth trend between the amplitude of the instantaneous radial force and the tool wear length with respect to the edge trimming length. The correlation coefficient between the instantaneous radial force and the tool wear length data is above 0.975. The results prove that the proposed instantaneous radial force in the feature dataset is sensitive to the tool wear length, and therefore is an effective indicator for tool wear prediction in edge trimming of UD CFRP. 31 Figure 3.11 Tool wear and instantaneous radial force vs cutting length under different cutting conditions and fiber cutting angle. (a) 𝑽𝑽𝒄𝒄 = 100 [m/min], 𝒇𝒇𝒛𝒛 = 0.05 [mm/tooth], 𝜷𝜷 = 70 [°]; (b) 𝑽𝑽𝒄𝒄 = 150 [m/min], 𝒇𝒇𝒛𝒛 = 0.1 [mm/tooth], 𝜷𝜷 = 90 [°]; (c) 𝑽𝑽𝒄𝒄 = 200 [m/min], 𝒇𝒇𝒛𝒛 = 0.075 [mm/tooth], 𝜷𝜷 = 170 [°]; (d) 𝑽𝑽𝒄𝒄 = 250 [m/min], 𝒇𝒇𝒛𝒛 = 0.025 [mm/tooth], 𝜷𝜷 = 135 [°]. 3.5.2 Effect of Hyperparameters on the Prediction Performance The performance of the two machine learning models in tool wear length prediction is evaluated by the mean absolute error in percentage 𝑀𝑀𝐴𝐴𝐸𝐸% and coefficient of determination 𝑅𝑅2, expressed by Eqs. (3.16) and (3.17). 32 𝑀𝑀𝐴𝐴𝐸𝐸% = �𝑦𝑦𝑙𝑙𝑒𝑒𝑝𝑝 − 𝑦𝑦𝑝𝑝𝑝𝑝����������������𝑦𝑦�𝑙𝑙𝑒𝑒𝑝𝑝× 100 (3.16) 𝑅𝑅2 = 1 − ∑�𝑦𝑦𝑙𝑙𝑒𝑒𝑝𝑝 − 𝑦𝑦𝑝𝑝𝑝𝑝�2∑�𝑦𝑦𝑙𝑙𝑒𝑒𝑝𝑝 − 𝑦𝑦�𝑙𝑙𝑒𝑒𝑝𝑝�2 (3.17) where 𝑦𝑦𝑙𝑙𝑒𝑒𝑝𝑝 is the measured result in the experiment, and 𝑦𝑦𝑝𝑝𝑝𝑝 is the predicted result from the machine learning models, 𝑦𝑦�𝑙𝑙𝑒𝑒𝑝𝑝 is the mean value of the measured results in the experiment. The 𝑀𝑀𝐴𝐴𝐸𝐸% presents the percentage error between the measured and the predicted results. The coefficient of determination 𝑅𝑅2 is related to the variation of response variables predicted by the machine learning models. Lower 𝑀𝑀𝐴𝐴𝐸𝐸% and higher 𝑅𝑅2 values indicate a higher predictive performance of the model. The performance of the machine learning models depends on the selection of the hyperparameters. For the RF model, the hyperparameters include the number of trees and the “feature bagging” size. To evaluate the performance of the hyperparameter tuning, 90% of the dataset is used as training dataset for hyperparameter tuning, and the remaining 10% of the dataset is used as test dataset for model performance evaluation. The 5-fold cross-validation is used to evaluate the prediction accuracy in the hyperparameter tuning. In the RF model, a smaller number of trees results in a larger error between the actual tool wear length and the predictions. When the number of the regression trees exceeds a certain value, a further increase of the tree number does not effectively improve the model performance but reduces the computational efficiency. Each regression tree grows based on the “feature bagging” procedure, where the randomly sampled feature subsets are selected to find the best split of the total features to reduce the correlation of the trees. In this study, the “feature bagging” size is tuned first under a large number of trees to focus on its individual effect on the prediction performance. 33 A range of “feature bagging” size from 2 to 7 is used with 4000 regression trees for data training in RF, with the 𝑀𝑀𝐴𝐴𝐸𝐸% results shown in Figure 3.12. It is shown that the RF model generates the lowest 𝑀𝑀𝐴𝐴𝐸𝐸% when the “feature bagging” size is 6. Afterwards, the number of the regression trees is tuned in the range from 500 to 4000 with an interval of 100 with the “feature bagging” size of 6. The 𝑀𝑀𝐴𝐴𝐸𝐸% results with the tree numbers are shown in Figure 3.13. It is found that the 𝑀𝑀𝐴𝐴𝐸𝐸% decreases when the tree number increases from 800 to 3000, and the model performance does not improve effectively after 3000. Therefore, the hyperparameter tuning results for the RF-based model are the “feature bagging” size of 6, and the regression tree number of 3000. In the ANN model, the hyperparameter is the number of neurons in the hidden layer. In the hyperparameter turning for ANN model, the neuron number in the hidden layer is selected as 4, 8, 16, 32, 64, 128, 256, with the corresponding 𝑀𝑀𝐴𝐴𝐸𝐸% results shown in Figure 3.14. When there are 128 neurons in the hidden layer, the ANN model generates the lowest 𝑀𝑀𝐴𝐴𝐸𝐸%. Further increase of the neuron number in the hidden layer causes overfitting and increases the prediction error. Therefore, the ANN model with 128 neurons in the hidden layer is used for the tool wear prediction. 34 Figure 3.12 𝑴𝑴𝑴𝑴𝑴𝑴% of tool wear prediction with “feature bagging” size in RF model. Figure 3.13 𝑴𝑴𝑴𝑴𝑴𝑴% of tool wear prediction with regression tree number in RF model. 35 Figure 3.14 𝑴𝑴𝑴𝑴𝑴𝑴% of tool wear prediction with neuron number of hidden layer in ANN model. 3.5.3 Performance Evaluation of Predictive Models Based on the hyperparameter tuning results, the comparisons between the predicted and the measured tool wear lengths in the test dataset using the RF and ANN models are presented in Figure 3.15 and Figure 3.16. The results show that the prediction error from the RF model is smaller compared to the ANN model. In addition, different partitions of the input data are selected randomly as the training and test datasets, with the 𝑀𝑀𝐴𝐴𝐸𝐸% and 𝑅𝑅2 results from the RF and ANN models presented in Table 3.4. It is shown that the RF model results in smaller 𝑀𝑀𝐴𝐴𝐸𝐸% and larger 𝑅𝑅2 compared to the ANN model for all the training sizes. Therefore, the RF model results in more accurate predictions of tool flank wear for the edge trimming of UD CFRP compared to ANN model developed in this study based on the same training dataset. 36 Figure 3.15 Comparison of predicted and measured tool wear lengths using RF model. Figure 3.16 Comparison of predicted and measured tool wear lengths using ANN model. 37 Table 3.4 Performance of tool wear predictive models developed based on machine learning algorithms. Training size 𝑴𝑴𝑴𝑴𝑴𝑴% 𝑹𝑹𝟐𝟐 RF ANN RF ANN 50% 10.61% 11.85% 0.845 0.763 60% 9.57% 11.74% 0.871 0.799 70% 8.63% 10.94% 0.885 0.824 80% 8.18% 10.72% 0.886 0.831 90% 7.41% 9.93% 0.902 0.850 Based on the data training with a constant radial depth of cut of 2 mm, the RF model is used to predict the tool wear length at other radial depths with experimental comparison. Table 3.5 shows the experimental cutting conditions with 1 mm and 3 mm radial depths of cut. Six tool immersion angles (155°, 157.5°, 160°,162.5°, 165°, 167.5°) are used for 3 mm radial depth of cut to obtain the instantaneous radial forces, and three tool immersion angles (162.5°, 165°, 167.5°) are used for 1 mm radial depth of cut due to the relatively smaller range between the entrance and exit immersion angles. Under each cutting condition, the instantaneous parameters are fed into the RF model with the data trained with 2 mm radial depth, and the tool flank wear lengths are predicted and compared with the experimental results. The average values of the predicted tool wear lengths at different tool immersion angles are calculated for experimental comparison under the same cutting conditions. The results show that the prediction errors are less than 10% for the edge trimming processes at different tested radial depths of cut, without the need for experimental data training at various radial depths. 38 Table 3.5 Cutting conditions and tool wear predictions with experimental comparison under different radial depths of cut. (𝑽𝑽𝑽𝑽𝒑𝒑𝒑𝒑: predicted tool flank wear length; 𝑽𝑽𝑽𝑽����𝒑𝒑𝒑𝒑: average value of predicted tool flank wear length; 𝑽𝑽𝑽𝑽𝒆𝒆𝒆𝒆𝒑𝒑: experimentally measured tool flank wear length) 𝑽𝑽𝒄𝒄 [m/min] 𝒇𝒇𝒛𝒛 [mm/tooth] 𝜽𝜽 [°] 𝒂𝒂𝒆𝒆 [mm] 𝝓𝝓 [°] 𝑽𝑽𝑽𝑽𝒑𝒑𝒑𝒑 [μm] 𝑽𝑽𝑽𝑽����𝒑𝒑𝒑𝒑 [μm] 𝑽𝑽𝑽𝑽𝒆𝒆𝒆𝒆𝒑𝒑 [μm] Error 100 0.05 90 1 162.5 47.6 50.2 46.17 8.75% 165 51.02 167.5 52 100 0.05 90 3 155 31.74 39.95 40.3 0.09% 157.5 30.46 160 44.3 162.5 44.66 165 44.37 167.5 44.18 200 0.05 90 3 155 18.02 19.46 21.13 7.8% 157.5 18.03 160 17.83 162.5 18.49 165 20.98 167.5 23.41 3.6 Conclusions This chapter presents the tool flank wear prediction in edge trimming of UD CFRP using machine learning techniques. The instantaneous parameters such as the instantaneous radial force, uncut chip thickness, and fiber cutting angle are used as the feature parameters. The design of experiment method with different cutting speeds, feed rates, and fiber orientations is used to conduct the edge trimming experiments for constructing a dataset of size 960 composed of seven 39 feature and one label parameters. Two machine learning models including RF and ANN are developed for data training and tool wear prediction. The correlation results prove that the instantaneous radial force is an effective indicator of the tool wear progression with respect to the cutting length. Based on the hyperparameter tuning results, the mean absolute error and the coefficient of determination are used to compare the prediction performance between the RF and ANN models. The comparison results under various data training sizes show that the RF model results in more accurate predictions of the tool wear length compared to the ANN model. Based on the training data from edge trimming with 2 mm radial depth of cut, the instantaneous radial forces collected with 1 mm and 3 mm radial depths of cut are used to predict the corresponding tool wear lengths from the RF model. The predictions are compared with the experimental results under several edge trimming conditions with less than 10% error, without the need for data training at various radial cutting depths. 40 Chapter 4: Tool Wear Monitoring for Edge Trimming of Multidirectional CFRP 4.1 Overview Multidirectional (MD) CFRP is a stack of UD CFRP layers arranged in a certain layer-up sequence with different fiber orientations along the thickness direction, in order to provide required strengths in multiple directions. Figure 4.1 shows an example of a quasi-isotropic MD CFRP plate, which is composed of the UD layers with the fiber orientations in the sequence of 0°/135°/45°/90°. Figure 4.1 Schematic view of a quasi-isotropic MD CFRP plate. The variation of fiber orientation along the thickness direction of the MD CFRP plate results in different relationships between the tool wear length and the edge trimming force in each UD layer. On the other hand, only the overall cutting forces are measurable, which are contributed by the force components at individual layers. The tool helix angle and the fiber orientation variations also result in the difference of the cutting forces in addition to the tool wear effect. Therefore, it is challenging to perform tool wear prediction or monitoring using the overall measured forces. In 41 this chapter, the tool wear length ratios corresponding to different UD layers of the MD CFRP are first analyzed and experimentally identified. For each UD layer, the cutting force is predicted from the tool wear length using the ANN and RF machine learning models, and the training data is based on the edge trimming experiments of UD CFRP in Chapter 3. The overall instantaneous radial force in edge trimming of MD CFRP is predicted from the force components at each UD layer, given the predefined maximum tool wear length along the edge. The tool helix angle which causes the change of instantaneous uncut chip thickness along the thickness direction is considered. The monitoring of the tool wear length can be achieved by comparing the force prediction from the predefined maximum tool wear length and the experimentally measured force during edge trimming of MD CFRP. Since the training data in the machine learning models are only based on edge trimming of UD CFRP, the proposed method does not require additional data training experiments for MD CFRP with different layer-up configurations. Therefore, the associated material consumptions and labor costs can be reduced extensively. The workflow to achieve the tool wear monitoring through force prediction in edge trimming of MD CFRP is shown in Figure 4.2. 42 Figure 4.2 Workflow of tool wear monitoring in edge trimming of CFRP based on force prediction 4.2 Machine Learning Algorithms for Force Prediction in Edge Trimming of MD CFRP 4.2.1 Tool Wear Characteristics in Edge Trimming of MD CFRP The workpiece used in the edge trimming experiments is a quasi-isotropic MD CFRP composite plate with fiber model UTS-700 and epoxy resin NCT-1. The size of the CFRP workpiece is 50.8 mm×50.8 mm×4.8 mm. The specifications of each UD layer of the workpiece are the same as the UD CFRP used in Chapter 3. There are 15 UD CFRP layers in the quasi-isotropic CFRP plates, and the fiber orientations of the UD CFRP layer are arranged in the sequence of 0°/90°/±45°/0°/±45°/90°/±45°/0°/±45°/90°/0° from top to bottom of the CFRP plate. The edge trimming experiments were conducted on the FADAL VMC-2216 3-axis CNC milling machine. The cutting inserts and the instruments for tool wear and force measurements are the same as the setup used in edge trimming of UD CFRP. A schematic of the edge trimming of the MD CFRP workpiece is shown in Figure 4.3. The measured edge trimming force in the experiment 43 is the summation of the force components contributed by each layer, which is in the form of UD CFRP [49]. Figure 4.3 Schematic of quasi-isotropic CFRP workpiece in edge trimming process. After each edge trimming path, the insert was uninstalled from the cutting tool, and the tool wear length was measured under an optical microscope. Figure 4.4 shows the tool wear length distribution along the tool edge, with the label showing the layer number which the tool edge portion is in cut. For the tool edge portion corresponding to each UD CFRP layer, the tool wear lengths at four locations were measured, and the average of the four values was recorded. The figure shows highly non-uniform distribution of the tool wear length, which is because different fiber orientations at various layers result in different tool wear rates along the tool edge. Therefore, the cutting force coefficients and the instantaneous forces corresponding to each layer are different considering the fiber orientation and the tool wear effect. In fact, the maximum tool wear length along the tool edge is the most significant because it determines the tool life. 44 Figure 4.4 An example of tool flank wear along the cutting edge in edge trimming of MD CFRP. (𝑽𝑽𝒄𝒄 = 100 [m/min]; 𝒇𝒇𝒛𝒛 = 0.05 [mm/tooth]; No.: UD layer number; 𝜽𝜽𝒍𝒍𝒂𝒂𝒍𝒍𝒆𝒆𝒓𝒓: fiber orientation of UD layer) According to Kim et al. [50], the tool flank wear rate is a function of the cutting force and tool wear length, expressed as: 𝑚𝑚𝑉𝑉𝐵𝐵𝑚𝑚𝑑𝑑= 𝐾𝐾𝑤𝑤𝐶𝐶𝑡𝑡 𝐹𝐹𝑙𝑙2𝑎𝑎𝑝𝑝 1𝑉𝑉𝐵𝐵 (4.1) where 𝑑𝑑 is the cutting length, 𝐾𝐾𝑤𝑤 is the tool wear coefficient determined by the mechanical properties of CFRP, 𝐶𝐶𝑡𝑡 is the coefficient determined by the rake and clearance angles of the insert, 𝑎𝑎𝑝𝑝 is the axial depth of cut, 𝐹𝐹𝑙𝑙2 is the contact force in the pressing zone as shown in Figure 4.5, which is determined by the average normal stress 𝜎𝜎 in the contact area between the tool flank wear and the workpiece, and is expressed by: 𝐹𝐹𝑙𝑙2 = 𝜎𝜎𝑎𝑎𝑝𝑝𝑉𝑉𝐵𝐵 (4.2) 45 Figure 4.5 Schematic of the relationship between tool flank wear length and tool geometry in edge trimming of a UD layer of CFRP. Assume the flank wear lengths corresponding to two different UD layers are 𝑉𝑉𝐵𝐵𝑖𝑖 and 𝑉𝑉𝐵𝐵𝑗𝑗, respectively. The ratio of the two flank wear rates is determined according to Eqs. (4.1) and (4.2) as: 𝑚𝑚𝑉𝑉𝐵𝐵𝑖𝑖 𝑚𝑚𝑑𝑑⁄𝑚𝑚𝑉𝑉𝐵𝐵𝑗𝑗 𝑚𝑚𝑑𝑑⁄= 𝐾𝐾𝑤𝑤𝑖𝑖𝐾𝐾𝑤𝑤𝑗𝑗𝜎𝜎𝑖𝑖𝜎𝜎𝑗𝑗(4.3) Because the contact region mainly involves elastic deformation of the workpiece, the ratio of the average normal stresses in the contact region is assumed to be equal to the ratio of elastic moduli of the CFRP material in the two UD layers. As a result, Eq. (4.3) is updated as: 𝑚𝑚𝑉𝑉𝐵𝐵𝑖𝑖𝑚𝑚𝑉𝑉𝐵𝐵𝑗𝑗= 𝐾𝐾𝑤𝑤𝑖𝑖𝐾𝐾𝑤𝑤𝑗𝑗𝐸𝐸𝑖𝑖𝐸𝐸𝑗𝑗(4.4) where 𝐸𝐸𝑖𝑖 and 𝐸𝐸𝑗𝑗 are the elastic moduli of the two UD CFRP layers, respectively. Since the fiber and matrix materials in each UD layer are the same, the variation of the elastic modulus in different UD layers is only caused by different fiber orientations. Assuming zero tool wear length with a 46 new tool at the beginning of the edge trimming process, the tool flank wear length ratio between the two UD layers for the same cutting length is expressed as 𝑉𝑉𝐵𝐵𝑖𝑖(𝑑𝑑)𝑉𝑉𝐵𝐵𝑗𝑗(𝑑𝑑) = 𝐾𝐾𝑤𝑤𝑖𝑖𝐾𝐾𝑤𝑤𝑗𝑗 𝐸𝐸𝑖𝑖𝐸𝐸𝑗𝑗 (4.5) Eq. (4.5) shows that the ratio of the tool flank wear length between two different UD layers only depends on the mechanical properties of CFRP materials in the UD layers, which can be approximated by a constant value with respect to the cutting length. Therefore, the tool wear length for each UD layer can be determined based on the ratio, as long as the maximum tool wear length along the tool edge is known. In this thesis, machine learning algorithms are used to develop the quantitative relationship between the wear length and the corresponding force component for each UD layer, thus establishing the relationship between the maximum tool wear length and the total cutting force by summing up individual force components at each UD layer with the corresponding tool wear length. This enables the tool life monitoring using the force measurement in edge trimming of MD CFRP that the decision of tool replacement is executed once the measured force achieves the critical value corresponding to the end of tool life, which is determined by the predefined maximum tool wear length. Experimental analyses on the tool wear length ratios corresponding to different UD layers are performed in edge trimming of MD CFRP, with the measured tool wear length distribution under two different cutting speeds and feed rates shown in Figure 4.6. Based on the layout configuration in Figure 4.4, the MD CFRP plate includes 4 UD layers with 0°, 45°, 135° fiber orientations, respectively, and 3 UD layers with 90° fiber orientation. The experimental results show that the maximum tool wear length always occurs at the layer with 45° fiber orientation. The average tool wear length of UD layers with different fiber orientations is in the order of 𝑉𝑉𝐵𝐵45° > 𝑉𝑉𝐵𝐵0° >47 𝑉𝑉𝐵𝐵135° > 𝑉𝑉𝐵𝐵90°. This result is consistent with that in edge trimming experiments reported in [50]. Figure 4.7. shows the ratios between the tool wear lengths of each UD layer with respect to the maximum tool wear length. The average ratios to the maximum tool wear length of the UD layers with 45°, 0°, 135°, and 90° fiber orientations are identified to be 0.899, 0.796, 0.692, and 0.531, respectively. The MAE% between the calculated tool wear length with the average ratios and the actual measurement of the tool wear length is 7.82%. The variation shown in Figure 4.7 may originate from the measurement error and the property variations in each UD layer. Moreover, the fiber orientations of the neighbouring layers for the UD layer with the same fiber orientation can be different, which can impose different boundary conditions along the thickness direction and influence the wear progression. In this thesis, the tool wear length ratios are assumed to be only dependent on the mechanical properties and fiber orientations of the UD layers, and the effect of the constraint from the neighboring UD layers in the thickness direction is neglected. Figure 4.6 Measurement results of tool wear length in edge trimming of MD CFRP: (a) 𝑽𝑽𝒄𝒄 = 100[m/min], 𝒇𝒇𝒛𝒛 = 0.05[mm/tooth]; (b) 𝑽𝑽𝒄𝒄 = 200[m/min], 𝒇𝒇𝒛𝒛 = 0.025[mm/tooth] 48 Figure 4.7 Ratios between tool wear lengths of UD layers and the corresponding maximum tool wear length: (a) 𝑽𝑽𝒄𝒄 = 100[m/min], 𝒇𝒇𝒛𝒛 = 0.05[mm/tooth]; (b) 𝑽𝑽𝒄𝒄 = 200[m/min], 𝒇𝒇𝒛𝒛 = 0.025[mm/tooth] 4.2.2 Machine Learning Models for Force Prediction in Edge Trimming of MD CFRP Machine learning algorithms are used to develop the quantitative relationship between the instantaneous radial force and the tool wear length for each UD layer of the MD CFRP workpiece. The instantaneous uncut chip thickness and fiber cutting angle for each UD layer are first calculated from the corresponding tool immersion angle. These parameters are fed into the machine learning models which are developed based on the experimental data in edge trimming of UD CFRP conducted in Chapter 3. The labeled dataset 𝐷𝐷′ = {(𝑥𝑥′, 𝑦𝑦′)} is the input of the machine learning algorithms, where 𝑥𝑥′ includes the tool wear length 𝑉𝑉𝐵𝐵, cutting speed 𝑉𝑉𝑟𝑟, uncut chip thickness ℎ, and fiber cutting angle 𝛽𝛽, 𝑦𝑦′ is the mean value of the instantaneous radial force 𝐹𝐹𝑚𝑚𝑙𝑙𝑙𝑙𝑚𝑚. It should be noted that the dataset fed into the machine learning models are different from those in Chapter 3, which uses the radial force as the feature parameter, and tool wear length as the label. 49 It should be noted that the axial depth of cut used in the edge trimming of UD CFRP is the same as the workpiece thickness (4.8 mm). However, the thickness of each UD layer in MD CFRP is 0.32 mm, which is 1/15 of the total thickness. Therefore, the predicted instantaneous radial forces from the machine learning models for UD CFRP need to be transformed to the forces for UD layers in MD CFRP. Based on the mechanistic model of milling of UD CFRP, Karpat [17] and Mullin [18] modeled the cutting coefficients (𝐾𝐾𝑙𝑙𝑟𝑟 and 𝐾𝐾𝑙𝑙𝑙𝑙) as sinusoidal functions of the fiber cutting angle, expressed as: 𝐾𝐾𝑙𝑙𝑟𝑟 = 𝐴𝐴1 sin(2𝛽𝛽 + 𝐵𝐵1) + 𝐶𝐶1 (4.6) 𝐾𝐾𝑙𝑙𝑙𝑙 = 𝐴𝐴2 sin(2𝛽𝛽 + 𝐵𝐵2) + 𝐶𝐶2 (4.7) where 𝛽𝛽 is the fiber cutting angle, and the other parameters (𝐴𝐴1, 𝐵𝐵1, 𝐶𝐶1, 𝐴𝐴2, 𝐵𝐵2, 𝐶𝐶2) are dependent on the mechanical properties of the CFRP workpiece and the cutting speed. Owing to non-zero helix angle of the cutting tool, the instantaneous immersion angle varies along the tool edge, resulting in differences in the uncut chip thicknesses and fiber cutting angles among different UD layers. In this thesis, it is proposed that the force components at the middle position along the layer thickness direction in edge trimming of UD CFRP can be represented by the average value of the overall radial force along the layer thickness direction. The details of the validation procedures are provided in Appendix A. Therefore, the radial force in edge trimming of UD CFRP with 4.8 mm thickness reduces proportionally when the thickness reduces to 0.32 mm (UD layer thickness of MD CFRP), as long as the UD layer shares the same instantaneous process parameters with the middle location of UD CFRP. The cutting parameters and the tool wear length at each UD layer of the MD CFRP are fed into the machine learning models as the input features (𝑉𝑉𝐵𝐵, 𝑉𝑉𝑟𝑟, ℎ, 𝜃𝜃). The instantaneous uncut chip thickness and the fiber cutting angle in the input features correspond to the parameters in the 50 middle location of the UD CFRP along the thickness direction. RF and ANN algorithms are used to develop data-driven models for the force prediction based on the tool wear length. For the RF model, regression trees are used as the decision trees, and the architecture of the RF model is shown in Figure 4.8. The feature splitting criterion in each regression tree is the same as that described in section 3.4.2. However, the difference compared to the RF model in section 3.4.2 is that the tool wear length is in the feature vector, and the instantaneous radial force is included as the output. Figure 4.8 Architecture of RF-based predictive model for instantaneous radial force For the ANN model, the feed-forward neural network with back-propagation training method is applied, and the architecture is shown in Figure 4.9. There are four neurons in the input layer (𝑉𝑉𝐵𝐵 , 𝑉𝑉𝑟𝑟 , ℎ , 𝜃𝜃 ), and one neuron in the output layer, which represents the mean value of the instantaneous radial force 𝐹𝐹𝑚𝑚𝑙𝑙𝑙𝑙𝑚𝑚. The weights in the network are adjusted in the same way as 51 described in section 3.4.3, and the neuron number of the hidden layer is tuned by the grid search method to achieve the best prediction performance. Figure 4.9 Architecture of ANN-based predictive model for instantaneous radial force 4.3 Experimental Comparison and Discussions 4.3.1 Process Conditions for Experimental Comparison Based on the training of the machine learning models with the experimental data in edge trimming of UD CFRP, the overall instantaneous radial forces in edge trimming of MD CFRP are predicted, and the results are compared with the measured forces under two cutting conditions for verification purpose. In the experiments, the radial depth of cut is 2 mm. The flank wear length on the insert was measured after each edge trimming path of 50.8 mm, and a total of 3 trimming passes were conducted at each cutting condition. The instantaneous radial forces were measured at three tool immersion angles (155°, 160°, 165°). As a result, the validation for the force prediction 52 using the machine learning models includes 2×3×3×15=270 datasets. The dataset includes the cutting speed, the instantaneous uncut chip thickness, and the fiber cutting angle for each UD layer, with the values listed in Table 4.1. For the tool wear length values, only the maximum tool wear length along the edge was measured from the experiment. With the identified average wear length ratios in section 4.2.1, the tool wear lengths corresponding to the UD layers with 0°, 90°, 135°, and 45° fiber orientations are calculated and listed in Table 4.2. The feature parameters are then fed into the machine learning models with the structure shown in Figure 4.8 and Figure 4.9 to predict the radial force component in each UD layer. The vector summation of the force components represents the prediction of the overall instantaneous radial force. The detailed calculation procedure is included in Appendix A. Table 4.1 Process parameters in validation dataset from UD layers in MD CFRP workpiece. 𝑽𝑽𝒄𝒄 [m/min] 𝒇𝒇𝒛𝒛 [mm/tooth] 𝜽𝜽𝒍𝒍𝒂𝒂𝒍𝒍𝒆𝒆𝒓𝒓 [°] 𝝓𝝓 [°] 155 160 165 𝒉𝒉 [mm] 𝜷𝜷 [°] 𝒉𝒉 [mm] 𝜷𝜷 [°] 𝒉𝒉 [mm] 𝜷𝜷 [°] 100 0.05 0 24.1 151.18 20.19 156.18 16.13 161.18 90 23.68 61.73 19.75 66.73 15.68 71.73 45 23.26 17.27 19.32 22.27 15.22 27.27 135 22.84 107.82 18.88 112.82 14.77 117.82 0 22.42 153.37 18.44 158.37 14.31 163.37 45 21.99 18.91 17.99 23.91 13.86 28.91 135 21.56 109.46 17.55 114.46 13.4 119.46 90 21.13 65 17.1 70 12.94 75 135 20.7 110.55 16.65 115.55 12.48 120.55 45 20.27 21.09 16.2 26.09 12.02 31.09 0 19.83 156.64 15.75 161.64 11.56 166.64 53 135 19.39 112.18 15.3 117.18 11.09 122.18 45 18.95 22.73 14.85 27.73 10.63 32.73 90 18.51 68.27 14.39 73.27 10.16 78.27 0 18.07 158.82 13.94 163.82 9.7 168.82 200 0.025 0 12.05 151.18 10.1 156.18 8.06 161.18 90 11.84 61.73 9.88 66.73 7.84 71.73 135 11.63 107.27 9.66 112.27 7.61 117.27 45 11.42 17.82 9.44 22.82 7.39 27.82 0 11.21 153.37 9.22 158.37 7.16 163.37 135 11 108.91 9 113.91 6.93 118.91 45 10.78 19.46 8.77 24.46 6.7 29.46 90 10.57 65 8.55 70 6.47 75 45 10.35 20.55 8.33 25.55 6.24 30.55 135 10.13 111.09 8.1 116.09 6.01 121.09 0 9.91 156.64 7.88 161.64 5.78 166.64 45 9.7 22.18 7.65 27.18 5.55 32.18 135 9.48 112.73 7.42 117.73 5.32 122.73 90 9.26 68.27 7.2 73.27 5.08 78.27 0 9.03 158.82 6.97 163.82 4.85 168.82 Table 4.2 Tool wear lengths for UD layers in MD CFRP calculated with the identified average tool wear ratio. Cutting conditions 𝑽𝑽𝒄𝒄 [m/min] 𝒇𝒇𝒛𝒛 [mm/tooth] 𝑽𝑽𝒄𝒄 [m/min] 𝒇𝒇𝒛𝒛 [mm/tooth] 100 0.05 200 0.025 𝑽𝑽𝑽𝑽𝒎𝒎𝒂𝒂𝒆𝒆 [μm] 16.99 22.71 28.05 20.54 27.26 27.85 𝜽𝜽𝒍𝒍𝒂𝒂𝒍𝒍𝒆𝒆𝒓𝒓 [°] 𝑽𝑽𝑽𝑽𝒍𝒍𝒂𝒂𝒍𝒍𝒆𝒆𝒓𝒓 [μm] 0 13.52 18.08 22.32 16.35 21.69 22.17 90 9.02 12.06 14.89 10.91 14.47 14.79 135 11.75 15.72 19.41 14.21 18.86 19.27 45 15.1 20.19 24.93 18.26 24.23 24.76 54 4.3.2 Hyperparameter Tuning for the Machine Learning Models The mean squared error in percentage ( 𝑀𝑀𝐴𝐴𝐸𝐸% ) is used as the error criterion for hyperparameter tuning for the machine learning models. 100% of dataset from edge trimming of UD CFRP is used as the training dataset, and the dataset of the UD layers in edge trimming of MD CFRP is the test dataset for performance evaluation. 5-fold cross validation is used in the hyperparameter tuning process. For the RF model, the “feature bagging” size is tuned in a range from 2 to 4 with 4000 regression trees because there are only four features in the training dataset. Figure 4.10 shows that the lowest 𝑀𝑀𝐴𝐴𝐸𝐸% is generated when the “feature bagging” size is 3. The number of the regression trees in the RF model is then tuned, with the results shown in Figure 4.11. The 𝑀𝑀𝐴𝐴𝐸𝐸% decreases when the number of the regression tress increases until 3000, and the performance of RF-based model does not improve effectively afterwards. Therefore, the hyperparameter tuning process in the RF-based predictive model results in the regression tree number of 3000, and the “feature bagging” size of 3. 55 Figure 4.10 𝑴𝑴𝑴𝑴𝑴𝑴% of force prediction with “feature bagging” size in RF model. Figure 4.11 𝑴𝑴𝑴𝑴𝑴𝑴% of force prediction with regression tree number in RF model. 56 In the hyperparameter tuning for the ANN model, the 𝑀𝑀𝐴𝐴𝐸𝐸% results with the hidden layer neuron numbers of 4, 8, 16, 32, 64, 128, 256, 512 are presented in Figure 4.12. The results show that The ANN model with 256 neurons in the hidden layer generates the lowest 𝑀𝑀𝐴𝐴𝐸𝐸%. Therefore, the ANN-based model with 256 neurons in the hidden layer is developed for the prediction of the instantaneous radial force. Figure 4.12 𝑴𝑴𝑴𝑴𝑴𝑴% of force prediction with neuron number in hidden layer of ANN model. 4.3.3 Force Prediction from the Maximum Tool Wear Length Figure 4.13 shows the comparison between the predicted and experimental instantaneous forces under different edge trimming conditions of MD CFRP. The mean squared error in percentage (𝑀𝑀𝐴𝐴𝐸𝐸%) is used to evaluate the accuracy of the force prediction. The 𝑀𝑀𝐴𝐴𝐸𝐸% between the predicted force from the RF model and the experimental result is 27.79%, and the 𝑀𝑀𝐴𝐴𝐸𝐸% 57 between the predicted force from the ANN model and the experimental result is 6.32%. The results show that the ANN method achieves better force prediction performance with the predefined maximum tool wear length. Because the regression in the RF model is based on classification, the RF model does not predict the forces accurately corresponding to the tool wear lengths out of the range of the training dataset. For the cutting conditions in Figure 4.13, the tool wear lengths in the dataset for validation are all less than the smallest value in the range of the tool wear lengths in the training dataset. As a result, the predicted forces generated by the RF model are larger than the measured forces. Table 4.3 lists the comparison between the measured forces and the predicted forces from the ANN model. The maximum error between the predicted forces from the ANN model and the experimental values is within 20% for all the 18 edge trimming conditions, with an average error of 6.29%. The results show that that the ANN model is able to predict the cutting force based on the predefined maximum tool wear length with acceptable accuracy. In edge trimming operations of MD CFRP, the measured forces increase asymptotically with the tool wear progression. When the measured forces are equal to the force prediction based on the predefined tool wear length value, it indicates that the maximum tool wear length reaches the predefined threshold value, which enables the tool wear monitoring in edge trimming of MD CFRP. 58 Figure 4.13 Measured and predicted instantaneous radial force under different conditions. (a) 𝑽𝑽𝒄𝒄 = 100 [m/min], 𝒇𝒇𝒛𝒛 = 0.05 [mm/tooth], 𝝓𝝓 = 155[°]; (b) 𝑽𝑽𝒄𝒄 = 100 [m/min], 𝒇𝒇𝒛𝒛 = 0.05 [mm/tooth], 𝝓𝝓 = 160[°]; (c) 𝑽𝑽𝒄𝒄 = 100 [m/min], 𝒇𝒇𝒛𝒛 = 0.05 [mm/tooth], 𝝓𝝓 = 165[°]; (d) 𝑽𝑽𝒄𝒄 = 200 [m/min], 𝒇𝒇𝒛𝒛 = 0.025 [mm/tooth], 𝝓𝝓 = 155[°]; (e) 𝑽𝑽𝒄𝒄 = 200 [m/min], 𝒇𝒇𝒛𝒛 = 0.025 [mm/tooth], 𝝓𝝓 = 160[°]; (f) 𝑽𝑽𝒄𝒄 = 200 [m/min], 𝒇𝒇𝒛𝒛 = 0.025 [mm/tooth], 𝝓𝝓 = 165[°]. 59 Table 4.3 Comparison of the predicted force from the ANN model and the experimental results. Cutting conditions 𝑽𝑽𝑽𝑽𝒎𝒎𝒂𝒂𝒆𝒆 [N] 𝑭𝑭𝒓𝒓−𝒆𝒆𝒆𝒆𝒑𝒑 [N] 𝑭𝑭𝒓𝒓−𝑴𝑴𝑨𝑨𝑨𝑨 [N] Error 𝑽𝑽𝒄𝒄 [m/min] 𝒇𝒇𝒛𝒛 [mm/tooth] 𝝓𝝓 [°] 100 50 155 16.99 83.8 99.7 18.97 % 22.71 110 110.5 0.45 % 28.05 120 115.8 3.5 % 160 16.99 82.7 96.4 16.57 % 22.71 109 106.1 2.66 % 28.05 119.8 111.3 7.1 % 165 16.99 81.7 95 16.28 % 22.71 106.5 100 6.1 % 28.05 118.2 104.5 11.59 % 200 25 155 20.54 81.3 79.1 2.71 % 27.26 87.7 84.1 4.1 % 27.85 88.6 84.5 4.63 % 160 20.54 78.1 74.5 4.61 % 27.26 79.3 79.5 0.25 % 27.85 80.1 79.9 0.25 % 165 20.54 74.9 68.7 8.28 % 27.26 75.9 73.8 2.77 % 27.85 76.2 74.3 2.49 % Average 6.29% 4.4 Conclusions In this chapter, machine learning techniques are used to predict the instantaneous radial force in edge trimming of MD CFRP considering the tool wear progression. The tool wear length ratios at the tool edge portions corresponding to UD layers with different fiber orientations are 60 experimentally identified. Based on the training data from edge trimming of UD CFRP, machine learning techniques including RF and ANN algorithms are used to predict the radial forces corresponding to each UD layer, given the maximum tool wear length along the tool edge. The individual force component for each layer contributes to the overall radial force, which is influenced by the cutting speed, feed rate, tool helix angle, and wear length distribution along the tool edge. The predicted forces from the machine learning algorithms are compared with the experimental results, which show that the ANN algorithm achieves more accurate force prediction compared to RF. The proposed method enables the tool wear monitoring in edge trimming of MD CFRP with various layer sequences, as long as the tool wear length ratios corresponding to the UD layers with different fiber orientations are identified. Given the maximum tool wear length along the tool edge, the corresponding cutting force is predicted from the ANN model. When the measured cutting force during the edge trimming operation of MD CFRP reaches the prediction from the model, it indicates that the maximum tool wear length achieves the predefined threshold value. Because the data training is only from the edge trimming of UD CFRP, there is no need to perform data training for MD CFRP with different layer-up configurations, and the associated time, material, and labor costs can be reduced. 61 Chapter 5: Conclusions and Future Research Directions 5.1 Conclusions This thesis presents the tool wear prediction and monitoring in edge trimming of UD and MD CFRPs using the cutting forces. Due to the challenges in developing physical models for cutting force prediction considering the tool wear length and fiber orientation, data-driven approaches are applied based on ANN and RF machine learning algorithms. Considering the high costs in data training experiments with different cutting depths or different layer-up sequences in MD CFRP, this thesis proposes to use instantaneous process parameters as the feature parameters in the machine learning model, and use the training data in edge trimming of UD CFRP to predict the cutting forces for MD CFRP. Therefore, the tool wear prediction and monitoring are achieved with limited training data from the edge trimming experiments. For tool wear prediction in edge trimming of UD CFRP, a dataset of size 960 is collected for training and testing of the machine learning models. The dataset includes the cutting speed, instantaneous uncut chip thickness, fiber cutting angle, and instantaneous radial force as the feature parameters, and the tool wear length as the label parameter. A high correlation exceeding 0.95 is observed between the tool wear length and the instantaneous radial force. The RF model with 3000 regression trees and 6 “feature bagging” size and ANN-based model with 128 neurons in the hidden layer are developed through hyperparameter tuning. 𝑀𝑀𝐴𝐴𝐸𝐸% and 𝑅𝑅2 are used to evaluate the prediction performance of the two machine learning models. The results show that RF model achieves better tool wear length prediction than ANN model, with 𝑀𝑀𝐴𝐴𝐸𝐸% lower than 10% and 𝑅𝑅2 higher than 0.9. The RF model with the training data in edge trimming experiments at 2 mm radial depth predicts the tool wear length for edge trimming at 1 mm and 3 mm radial depths of cut with 62 less than 10% prediction error. This shows that the RF model is able to achieve tool wear prediction at various radial depths of cut without further experimental data training. The tool wear monitoring for edge trimming of MD CFRP is based on the cutting force prediction by the machine learning models including the tool wear effect. The non-uniform distribution of the tool wear length along the cutting tool edge due to different fiber orientations of the UD layers is considered. The wear length ratios among the tool edge portions corresponding to different UD layers are identified experimentally. Based on the training data from edge trimming experiments of UD CFRP and the predefined maximum tool wear length, ANN and RF models are developed to predict the instantaneous radial force for the individual UD layers. The vector summation of the force components represents the prediction of the overall instantaneous radial force. The experimental comparison shows that the ANN model achieves more accurate force predictions compared to the RF model. The maximum error between the predicted forces from the ANN model and the experimental results is within 20% for all the tested edge trimming conditions, and the average error is 6.29%. The force prediction is based on the dataset in edge trimming of UD CFRP, and no data training from edge trimming of MD CFRP is required. 5.2 Future Research Directions The tool wear prediction method in edge trimming of UD CFRP proposed in this thesis can only work under a constant axial depth of cut due to the limited edge trimming condition. When the axial depth of cut changes, the middle position along the thickness direction changes. Because the instantaneous radial force is the average radial force along the thickness direction corresponding to the uncut chip thickness and fiber cutting angle at the middle position, its value changes with different axial depths of cut. Therefore, the instantaneous radial force with a new 63 axial depth of cut cannot be directly calculated from the forces with the axial depth of cut in the constructed dataset. This work can be improved by integrating the physical model in milling of CFRP and the data-driven approach to achieve the transformation from the instantaneous radial force with the known axial depth of cut to the force at other axial depths of cut. Therefore, the tool wear prediction method in edge trimming of UD CFRP proposed in this study can be used for more general process conditions. Another potential future study is to include the boundary effect from neighboring layers along the workpiece thickness direction in identifying the tool wear length ratios for different UD layers in edge trimming of MD CFRP. This can be achieved through machine learning methods to build the relationship between the tool wear rate, the fiber orientation, and fiber layer-up configuration. 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[51] Altintas, Y., “Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design,” Cambridge University Press, 2012. 71 Appendix: Mechanics Model Transforming Force Prediction in Edge Trimming of UD CFRP to UD Layer Force in Edge Trimming of MD CFRP In the edge trimming experiments of UD CFRP, the helix angle of the insert 𝛾𝛾 is 20.53°, and the axial depth of cut 𝑎𝑎𝑝𝑝 is 4.8 mm. The helix angle of the insert causes a lag angle 𝜓𝜓 of the instantaneous tool immersion angle along the thickness direction of CFRP workpiece. The lag angle between the top and the bottom surfaces of the CFRP plate is calculated by 𝜓𝜓 = 2𝑡𝑡𝑎𝑎𝑛𝑛𝛾𝛾𝐷𝐷𝑡𝑡𝑎𝑎𝑝𝑝 = 8.24° (A. 1) where 𝐷𝐷𝑡𝑡 is the diameter of the milling tool (25 mm). Based on the mechanics model of milling process [51], the thickness of the CFRP workpiece is divided into 𝐻𝐻 segments, with the thickness of each element expressed as: ∆𝑎𝑎 = 𝑎𝑎𝑝𝑝𝐻𝐻(A. 2) In this study, 𝐻𝐻 is considered as an odd number. The uncut chip thickness and the fiber cutting angle are assumed to be constant in each segment with the thickness of ∆𝑎𝑎, and the elemental radial force contributed by each individual segment is calculated by ∆𝐹𝐹𝑙𝑙 = ∆𝑎𝑎(𝐾𝐾𝑙𝑙𝑟𝑟ℎ + 𝐾𝐾𝑙𝑙𝑙𝑙) (A. 3) By expanding Eqs (4.6) and (4.7), the cutting coefficients 𝐾𝐾𝑙𝑙𝑟𝑟 and 𝐾𝐾𝑙𝑙𝑙𝑙 are derived as: 𝐾𝐾𝑙𝑙𝑟𝑟 = 𝐴𝐴1 sin(2𝛽𝛽 + 𝐵𝐵1) + 𝐶𝐶1 = 𝐴𝐴1sin2𝛽𝛽cos𝐵𝐵1 + 𝐴𝐴1cos2𝛽𝛽sin𝐵𝐵1 + 𝐶𝐶1 (A. 4) 𝐾𝐾𝑙𝑙𝑙𝑙 = 𝐴𝐴2 sin(2𝛽𝛽 + 𝐵𝐵2) + 𝐶𝐶2 = 𝐴𝐴2sin2𝛽𝛽cos𝐵𝐵2 + 𝐴𝐴2cos2𝛽𝛽sin𝐵𝐵2 + 𝐶𝐶2 (A. 5) Based on the functions of cutting force coefficients in Eqs. (A.4) and (A.5), and the uncut chip thickness function ℎ = 𝑓𝑓𝑧𝑧 sin𝜙𝜙, the elemental force is calculated as 72 Δ𝐹𝐹𝑙𝑙 = (𝐴𝐴1cos𝐵𝐵1 𝑓𝑓𝑧𝑧𝑠𝑠𝑖𝑖𝑛𝑛𝜙𝜙 sin 2𝛽𝛽 + 𝐴𝐴1sin𝐵𝐵1𝑓𝑓𝑧𝑧𝑠𝑠𝑖𝑖𝑛𝑛𝜙𝜙 cos 2𝛽𝛽 = +𝐴𝐴2cos𝐵𝐵2 sin 2𝛽𝛽 + 𝐴𝐴2sin𝐵𝐵2 cos 2𝛽𝛽 + 𝐶𝐶1𝑓𝑓𝑧𝑧𝑠𝑠𝑖𝑖𝑛𝑛𝜙𝜙 + 𝐶𝐶2)Δ𝑎𝑎 (A. 6) In the edge trimming of MD CFRP, both the value and the direction of ∆𝐹𝐹𝑙𝑙 change at different segments along the thickness direction due to the change of the fiber cutting angle 𝛽𝛽 and uncut chip thickness ℎ. In this study, the direction of the total radial force 𝐹𝐹𝑙𝑙 is assumed to be aligned with the direction of the radial force at the middle segment. Therefore, the total radial force 𝐹𝐹𝑙𝑙 is the vector summation of ∆𝐹𝐹𝑙𝑙 contributed by individual segments, expressed by 𝐹𝐹𝑙𝑙 = �∆𝐹𝐹𝑙𝑙 cos𝜓𝜓𝑠𝑠𝐻𝐻𝑝𝑝=1(A. 7) 𝜓𝜓𝑠𝑠 = 𝜓𝜓𝐻𝐻 �𝐻𝐻 + 12 − 𝑝𝑝� (A. 8) where 𝜓𝜓𝑠𝑠 is the angle between the elemental force of each segment and the force of the middle segment. Combining Eqs. (A. 6) and (A. 7), it is derived that 𝐹𝐹𝑙𝑙Δ𝑎𝑎= 1Δ𝑎𝑎�∆𝐹𝐹𝑙𝑙 cos𝜓𝜓𝑠𝑠 = 𝐴𝐴1cos𝐵𝐵1� 𝑓𝑓𝑧𝑧𝑠𝑠𝑖𝑖𝑛𝑛𝜙𝜙 sin 2𝛽𝛽 cos𝜓𝜓𝑠𝑠𝐻𝐻𝑝𝑝=1+ 𝐴𝐴1sin𝐵𝐵1� 𝑓𝑓𝑧𝑧𝑠𝑠𝑖𝑖𝑛𝑛𝜙𝜙 cos 2𝛽𝛽 cos𝜓𝜓𝑠𝑠𝐻𝐻𝑝𝑝=1 = +𝐴𝐴2cos𝐵𝐵2� sin2𝛽𝛽 cos𝜓𝜓𝑠𝑠𝐻𝐻𝑝𝑝=1+ 𝐴𝐴2sin𝐵𝐵2� cos 2𝛽𝛽 cos𝜓𝜓𝑠𝑠𝐻𝐻𝑝𝑝=1+ 𝐶𝐶1� 𝑓𝑓𝑧𝑧𝑠𝑠𝑖𝑖𝑛𝑛𝜙𝜙 cos𝜓𝜓𝑠𝑠𝐻𝐻𝑝𝑝=1+ �𝐶𝐶2 cos𝜓𝜓𝑠𝑠𝐻𝐻𝑝𝑝=1 = 𝑞𝑞1� 𝑓𝑓𝑧𝑧𝑠𝑠𝑖𝑖𝑛𝑛𝜙𝜙 sin 2𝛽𝛽 cos𝜓𝜓𝑠𝑠𝐻𝐻𝑝𝑝=1+ 𝑞𝑞2� 𝑓𝑓𝑧𝑧𝑠𝑠𝑖𝑖𝑛𝑛𝜙𝜙 cos 2𝛽𝛽 cos𝜓𝜓𝑠𝑠𝐻𝐻𝑝𝑝=1 = +𝑞𝑞3� sin2𝛽𝛽 cos𝜓𝜓𝑠𝑠𝐻𝐻𝑝𝑝=1+ 𝑞𝑞4� cos 2𝛽𝛽 cos𝜓𝜓𝑠𝑠𝐻𝐻𝑝𝑝=1+ 𝑞𝑞5�𝑓𝑓𝑠𝑠𝑖𝑖𝑛𝑛𝜙𝜙 cos𝜓𝜓𝑠𝑠𝐻𝐻𝑝𝑝=1+ 𝑞𝑞6� cos𝜓𝜓𝑠𝑠𝐻𝐻𝑝𝑝=1(A. 9) 73 where 𝑞𝑞1, 𝑞𝑞2, 𝑞𝑞3, 𝑞𝑞4, 𝑞𝑞5, 𝑞𝑞6 are expressed by ⎩⎪⎨⎪⎧𝑞𝑞1 = 𝐴𝐴1cos𝐵𝐵1 𝑞𝑞2 = 𝐴𝐴1sin𝐵𝐵1𝑞𝑞3 = 𝐴𝐴2cos𝐵𝐵2𝑞𝑞4 = 𝐴𝐴2sin𝐵𝐵2 𝑞𝑞5 = 𝐶𝐶1𝑞𝑞6 = 𝐶𝐶2(A. 10) The immersion angle 𝜙𝜙 and fiber cutting angle 𝛽𝛽 at each integration step are calculated, and the summation expressed in Eq. (A.9) is obtained. Therefore, 𝑞𝑞1, 𝑞𝑞2, 𝑞𝑞3, 𝑞𝑞4, 𝑞𝑞5, 𝑞𝑞6 are obtained by least squares fitting based on the experimental data, and 𝐴𝐴1, 𝐵𝐵1, 𝐶𝐶1, 𝐴𝐴2, 𝐵𝐵2, 𝐶𝐶2 in Eqs. (4.1) and (4.2) are expressed by ⎩⎪⎪⎪⎪⎨⎪⎪⎪⎪⎧𝐴𝐴1 = 𝑞𝑞2sin(𝐵𝐵1) 𝐵𝐵1 = 𝑎𝑎𝑎𝑎𝑐𝑐𝑡𝑡𝑎𝑎𝑛𝑛 𝑞𝑞2𝑞𝑞1𝐶𝐶1 = 𝑞𝑞5𝐴𝐴2 = 𝑞𝑞2𝑠𝑠𝑖𝑖𝑛𝑛 (𝐵𝐵2) 𝐵𝐵2 = 𝑎𝑎𝑎𝑎𝑐𝑐𝑡𝑡𝑎𝑎𝑛𝑛 𝑞𝑞4𝑞𝑞3𝐶𝐶2 = 𝑞𝑞6(A. 11) Assuming the UD CFRP is made of 𝑧𝑧 layers, with the identified cutting coefficients 𝐾𝐾𝑙𝑙𝑟𝑟 and 𝐾𝐾𝑙𝑙𝑙𝑙, the radial force of the middle layer of UD CFRP is calculated as 𝐹𝐹𝑙𝑙−𝑚𝑚𝑖𝑖𝑝𝑝 = � ∆𝐹𝐹𝑙𝑙 cos𝜓𝜓𝑠𝑠𝐻𝐻+12 +𝐻𝐻−12𝑧𝑧𝐻𝐻+12 −𝐻𝐻−12𝑧𝑧(A. 12) Experimental data from UD CFRP trimming with the cutting speed of 150 m/min and similar tool wear length of 37 μm was selected for validating the assumption that the average radial force can be represented by the radial force at the middle layer. The integration thickness ∆𝑎𝑎 was considered as 0.01 mm. The cutting coefficients 𝐾𝐾𝑙𝑙𝑟𝑟 and 𝐾𝐾𝑙𝑙𝑙𝑙 were identified as 74 𝐾𝐾𝑙𝑙𝑟𝑟 = 1713.77 sin(2𝛽𝛽 + 52.08) + 770.69 𝐾𝐾𝑙𝑙𝑙𝑙 = 54.43 sin(2𝛽𝛽 − 74.68) + 55.61 The number of UD layers in the MD CFRP plate 𝑧𝑧 is 15, and then the middle layer is the 8th layer. The comparison between the instantaneous radial force of the 8th layer and 1/15 of the total instantaneous radial force is listed in Table A.1, which illustrated that the 1/15 of the total instantaneous radial force is very close to the instantaneous radial force of the 8th layer, with the average error of 0.36% . Therefore, the total predicted instantaneous radial force in edge trimming of MD CFRP can be calculated by: 𝐹𝐹𝑙𝑙 = � 115𝐹𝐹𝑙𝑙−𝑝𝑝𝑝𝑝 cos𝜓𝜓𝑙𝑙𝑧𝑧𝑝𝑝=1(A. 13) 𝜓𝜓𝑙𝑙 = 𝜓𝜓𝑧𝑧 �𝑧𝑧 + 12 − 𝑝𝑝� (A. 14) where 𝐹𝐹𝑙𝑙−𝑝𝑝𝑝𝑝 is the instantaneous radial force of UD layers obtained from the machine learning force predictive model, 𝜓𝜓𝑙𝑙 is the angle between the layer force and the force of the middle layer. Table A.1 Comparison between instantaneous radial force of the 8th layer and 1/15 of the total instantaneous radial force in edge trimming of UD CFRP. 𝑽𝑽𝒄𝒄 [m/min] 𝒇𝒇𝒛𝒛 [mm/tooth] 𝜽𝜽 [°] 𝝓𝝓 [°] 𝑭𝑭𝒓𝒓 [N] 𝟏𝟏 𝟏𝟏𝟏𝟏⁄ 𝑭𝑭𝒓𝒓 [N] 𝑭𝑭𝒓𝒓−𝟖𝟖 [N] Error 75 150 0.05 15 155 176.16 11.74 11.79 0.42% 150 0.05 15 157.5 170.74 11.38 11.41 0.26% 150 0.05 15 160 163.6 10.91 10.95 0.37% 150 0.05 15 162.5 155.51 10.37 10.4 0.29% 150 0.05 15 165 146.39 9.76 9.78 0.2% 150 0.05 15 167.5 136.27 9.08 9.11 0.33% 150 0.75 150 155 184.01 12.27 12.3 0.24% 150 0.75 150 157.5 183 12.2 12.21 0.08% 150 0.75 150 160 180.37 12.02 12.06 0.33% 150 0.75 150 162.5 176.1 11.74 11.81 0.59% 150 0.75 150 165 170.24 11.35 11.43 0.7% 150 0.75 150 167.5 162.6 10.84 10.9 0.55% Average 0.36%
Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Tool wear prediction and monitoring in edge trimming of carbon fiber reinforced polymer using machine learning methods
Text